A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback

Home healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs’ quality of care is evaluated using Medicare’s star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses’ ratings and reviews are the best representatives of organizations’ trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs’ data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients’ feedback using a combination of statistical and machine learning techniques. HHCAs’ data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered: SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of 97.37%. However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of 91.87%. Additionally, variable significance is derived from investigating each attribute’s importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with decision-making.


Introduction
Many people want to remain at home as they get older, especially those with disabilities. However, home healthcare services are becoming increasingly sophisticated and intensive. People with disabilities, chronic diseases, and functional impairments need additional services and support to maintain their independence. When it comes to fulfilling the needs and demands of these populations, home health organizations and other service providers are investigating new models of treatment and payment, as well as the optimum use of their workforce. It is important to consider where home healthcare fits into the wider healthcare system in light of these issues and possibilities. Individuals appreciate the benefits of receiving healthcare at home, and well-managed home healthcare can encourage healthy living and well-being [1].
Home healthcare agencies (HHCAs) are a network of treatment delivered to individuals in their residences by professional staff under the supervision of medical physicians. The Medicare rules are perceived as the standard treatment for all interactions between HHCAs, even if an individual is not insured by Medicare [2]. The star ratings in Medicare are utilized to measure the quality of the service HHCAs provide. According to the Centers for Medicare and Medicaid Services (CMS), over 5.26 million aged and impaired persons were cared for by 10,519 HHCAs throughout the USA in 2019 [3]. HHCAs are the most rapidly rising expenditure in the Medicaid beneficiaries due to the elderly population, a higher number of chronic illnesses, and increased hospital fees [4].
The high quality of services provided by HHCAs is an essential component for patients in order to improve the provided services. In Medicare, star ratings are of significance and are valuable to analyze the performance of quality services regarding the HHCA. It was proposed in July 2015 by CMS and named Quality of Patient Care star ratings (QoPCsr) with a range of 1-5; "1" means an awful experience about service, and "5" means the consumer is delighted about the system [5]. They implemented star ratings as a significant criterion for customers to consider when choosing a home care professional.
The world of HHCAs varies from clinics and other agencies, whereby nurses are employed. For this instance, home healthcare workers operate independently in the field with support services provided by a head office. The nurse-practitioner working partnership has considerably less physician communication, and the surgeon relies on the nurse to render decisions and communicate observations to a larger extent [6]. This high degree of patient control in the home environment and the minimal supervision of informal careers by skilled physicians motivated us to conduct research in HHCAs and uncover the influential features using the star ratings.
Another characteristic of HHCAs is that physicians deliver services in a special environment for everyone. There could be situational factors that pose costs for patients that the health professional cannot remove [7]. Hospitals should have offices for environmental protection to control air pollution, and engineers should guarantee that the staircase height is secure. Home care professionals are unlikely to be trained or have the means to identify and improve patient welfare threats at home. It is essential to determine all the factors for HHCAs with the star ratings to enhance their quality service and increase their revenue as well. Moreover, the influential factors are also helpful in satisfying the customer by providing better services in the Medicare environment.
Influential features are one the best ways to obtain favorable results from unstructured data. The identification of novel features concisely improves the measuring evaluators in the quantitative study. The study of HHCAs enables us to establish an effective framework by utilizing data mining techniques for exploring the best house healthcare facilities, as there is a need to declare an ML model that provides promising outcomes.
Numerous healthcare professionals at home wondered why they have poor star ratings considering comparable facilities and services. The poor satisfaction rate suggests that patients are reduced to home health services, significantly affecting the healthcare provider's income over time. Home practitioners' reputation could also be involved in the poor star ratings. Combined with this, it is challenging to continuously examine enormous amounts of data while discovering complex and dynamic characteristics that are likely to occur, but are obscure to humans [8]. The rising digitization of healthcare and the advancement in ubiquitous computing technology has hastened the development of prediction models for deriving knowledge from patient feedback for home care services. The retrieved knowledge could potentially be valuable to various user groups within the healthcare industry, ranging from patients to their respective healthcare practitioners [9].
Therefore, examining Medicare's star ratings of HHCAs' data is a need, and the use of artificial intelligence (AI) in gaining insights from these data will help in establishing an effective framework for evaluating the finest home healthcare facilities. Artificial intelli-gence, known as machine learning, or ML, can be defined as the application of a number of different statistical methods that can be used to produce predictions and decisions based on similarities between what is currently being examined and what is being identified in the past. To fill this gap, we propose an automated ML-based method for gaining insights from the OASIS and Medicaid claim datasets. The utilized approach will yield the influential factors that are highly dependent on the star ratings. A publicly available home healthcare agencies (HHCAs) dataset [10] is utilized to conduct the experimental phase using the different machine learning (ML) techniques. The research aims to gain valuable insights from an unstructured form of HHCAs' data. The unstructured data are firstly converged into a meaningful form so that machines can easily interpret the data. Then, various statistical techniques are brought into action to find the influential characteristics of the data. In this study, we employ binary and multi-class classification on four renowned machine learning algorithms. In addition, diving deeply into the study, the variable significance for each factor is computed to evaluate each feature's participation in the HHCAs' predictive model building. Healthcare-related data are frequently vast and challenging for individuals to swiftly evaluate and interpret. In order to identify and predict different ailments effectively, ML-based models have demonstrated promising outcomes in all medical domains [11]. Analyzing the HHCAs' data using data mining techniques along with ML approaches can assist in the creation of a framework for accurately identifying the best home healthcare facilities. A blend of statistical along with ML techniques is used to construct an automated predictive framework for extracting knowledge from patient input.
To emphasize the significance of our work, the following are the contributions that this study makes:

1.
Binary class and multi-class classification are applied by using four renowned ML models built to justify the robust model for HHCAs' data.

2.
Computing variable significance score for each attribute to analyze the contribution of each indicator in predictive analysis.

3.
For experimentation, unstructured data are considered, and statistical techniques are applied to uncover the outperforming indicators.

4.
Twelve effective attributes are proposed in this research, which can help in finding the best HHCA. We are the first to explore these features from HHCAs' data.
The rest of the study is structured as follows: Section 2 covers the literature review, where studies are considered regarding the role of ML in Medicare and the implication of Deep Learning in Medicare. In Section 3, the proposed methodology is discussed along with the proposed framework and a brief description of the data. In Section 4, experiments are performed, and the findings are analyzed in the Results Section. Finally, Sections 5 and 6 illustrate the conclusion and future work of the study, respectively.

Related Work
The way doctors and health givers think about disease and treatment has significantly evolved to reflect the changes that have taken place in our patients, our healthcare system, and medical science. The complexity level now present in medicine is beyond the capabilities of the human mind [12]. As a result, the healthcare industry has made substantial use of computer algorithms, which can learn from human decisions [13]. Health information technologies (HITs) have been widely regarded as critical for enhancing the quality of healthcare organizations [14][15][16]. Driven by the significant gains of these technological innovations, whether in clinical or IT fields, the governmental agencies in Europe and America have committed large financial resources to advance HITs' adoption in the healthcare sector [17,18]. Data analysis tools evaluate various data types and run relevant analyses to gain insights from data records. This is essential when it comes to translating raw patient data into useful information, which can be used to support the decision-making in healthcare organizations [19,20]. Delen [21] proposed a simple classification of analytics that distinguishes three types of analytics: descriptive, predictive, and prescriptive, each defined by the data type and the objective of the study. In the context of predictive analytics, it is possible to predict a particular variable's future by utilizing probabilistic modeling [22]. With predictive analytics, developers can access flexible and active predictive models for predictions for the future that identify causalities, trends, and hidden correlations between the input and target output. It is not hard to see how predictive analytics can be used in the healthcare industry to help healthcare providers understand the complexities of clinical cost, find the most effective treatment options, and anticipate future healthcare trend lines relying on the habits, lifestyles, and diseases of their patients [23]. Natural language processing (NLP) and Data Mining are mostly applied in predictive-analytics-based approaches [24,25].
Various researchers have studied the impact of important factors using ML techniques in prior studies. Most of the studies provide the solution for binary classification and regression analysis in the medical domain. Some important research to find the influential parameters using the ML methods are discussed here.
The start was to develop and verify a patient registration system for health promotion that allows patients to be classified according to their skilled nursing needs [26]. Nursing theory and experience were combined to create the Community Health Intensity Rating Scale (CHIRS). Groups of public health nursing experts created model definitions for fifteen public health criteria, and they used both patient attributes and essential measures of treatment as descriptive words of nursing care standards. The method was then put through its paces with the help of three home health agency support nurses. A cumulative rating of 560 graphs by two home health organizations was used to validate the system.
Prediction analytics and AI in healthcare face a number of obstacles, including data access, standardization, engagement, computing resource requirements, and the implementation of predictive models [27]. In addition, big data analysis has its own set of issues to deal with [17,28]. It has become increasingly important to explore the use of high-speed cloud computing for both data storage and maintenance, as well as for business intelligence, due to the recent growth of big data in medicine and the advancement of cloud computing [29]. Nevertheless, the analogies between cloud and non-cloud storage and maintenance of massive data are complex. Big data analysis, on the other hand, necessitates high-performance parallel distributed computing algorithms. Data science, bioinformatics, statistical genomics, and other fields have all attempted to address this issue. In addition to the aforementioned medical images and genomic data, these apps also allow for the study of organized data [29,30].
Moreover, rating quality was examined and how confidential rates react to the introduction of a five star rating system for nursing homes [31]. According to their findings, the difference in price among top and bottom facilities grows because of star ratings. The highest-level facility's prices rose by 4.7 to 6.1% higher than the lowest-level premises' prices in total. They see more fantastic pricing effects in less mature industries, where buyers can choose from a broader range of nursing homes. The findings show that customers are more receptive to quality reporting where the interface is streamlined and the audiences are less fragmented.
During the literature review, it was noted that there was no similar research on the star rating of reviews using Medicare data with the exception of one paper [32], where the research was conducted on review ratings, but in the context of patient outcomes and not on the influential factors. Therefore, the research being conducted in this paper is entirely novel in that sense. Many research papers identified the influential factors in other areas apart from the review ratings. Conceptually similar papers were selected for the literature review, and the findings are presented in this paper. The association between the Centres for Medicare and Medicaid Services Hospital Star Rating and Patient Outcomes was researched, which is somewhat related to the current study in terms of star ratings and Medicare providers [32]; however, this research is novel in the sense that it focuses just on the home healthcare rating and influential factors driving the rating.

Proposed Methodology
The methodology for the home healthcare agencies (HHCAs) framework is discussed in this section. Figure 1 supports the precise understanding of the whole approach regarding the methodology and analysis that we adopted to uncover the most influential factors. For this purpose, the HCCAs' dataset was considered, where four feature selection techniques were employed. First, data pre-processing was carried out on the available data. Second, the double results' interpretation approach was based on binary and multiclass classification problems utilizing the star ratings as the target variable. Third, four renowned ML models (Random Forest, Deep Neural Network, Support Vector Machine, Decision Tree) were implemented on the HHCA dataset to find the most robust binary and multi-class classification models separately. Moreover, the evaluation of ML models was assessed based on the accuracy, recall, precision, and F-1 score. Finally, the receiver operating characteristic curve (ROC) for each problem (binary, multi-class) is presented to analyze the varied threshold visually for a better understanding of the ML model.

Dataset Description
In this research, we considered the "Home Health Care Agencies (HHCA)" dataset, which is a directory of all Medicare-approved home care services [10]. It consists of "11,176" rows and "70" attributes, including the target variable. The name of the target variable is "Q_p_c_S_r", which is the ratings of the various customers that have a range of 1-5. In a real dataset, huge attribute names were renamed to shorten them for better understanding. New names for all attributes in the dataset and their old names are depicted in Table 1.
Identifying possible outliers is critical, and it is necessary to remove them before performing an analysis [34]. For this purpose, we utilized the Weka software with an "IQR" filter to remove the outliers from the dataset, and "4926" instances out of "11,176" were outliers in the dataset. These were removed from the dataset and made the dataset ready for predictive analysis. Finally, data preparation was performed successfully, and it selected the "41" features along with clean data for the next phase.

Problem Formulation
Two classifications were conducted on the dataset, including multi-class classification [35] and binary classification problems [36]. Star ratings are a multi-class classification problem with a range of 1-5 target classes in the dataset. Therefore, we propose the multi-class classification solution for the problem using the five classes on the dataset by implementing the ML techniques. On the other hand, we divided the star rating into two classes: the "bad" and "good" target class, making it a binary classification problem. On the other hand, we converted the target variable for the binary classification problem by setting the range of 1-3.2 as a bad class and all others as good classes. This division of target variables for binary classification was performed to make the dataset balanced. The unbalanced target class overfits the ML model, which leads to biasness [37]. Therefore, we made a better division for the binary target class to overcome this issue. The performance of the ML techniques was evaluated based on the confusion matrix and critically analyzed by interpreting the results for each model.
Predictive analysis is a significant part of this research for creating good enough and timely decisions using HHCAs using the five machine learning algorithms. In this case study, the HHCA dataset was considered for predictive analysis using feature selection and predictive analysis. We uncovered the most influential features and then built the predictive model using the vital feature. The Weka tool was considered the most powerful predictive analysis for building ML predictive models.
The proposed study utilized ML and DL models to show the effectiveness of the proposed features. SVM is a powerful algorithm due to its kernel techniques such as the linear, polynomial, and RBF kernel. These kernel tricks are used to transform the data from one feature pace to another feature space, where the data are more linearly separable [38]. Along with SVM, RF and DT were selected from the ML techniques for binary and multiclass classification problems. Moreover, from deep learning models, the DNN was selected, and the results were compared with other ML models. DNN performed well in binary classification compared to the other models.

Results
A deep analysis is conducted in this portion of the research. All the steps and outcomes from the previous methodology section are employed here to gain valuable insights from the HHCA dataset. Briefly, experiments were conducted on the available data by employing statistical techniques to uncover the helpful attributes from the available data. Moreover, a concise approach was performed using machine learning to build sophisticated models to obtain valuable outcomes from those outperformed characteristics.

Feature Subset Selection
This subsection performs the feature subset selection for HHCAs using the "40" features to find the most influential indicators. To fulfil this need, we considered the various filters of the WEKA tool for feature selection and dimension reduction. The ranker method and greedy search algorithms find the most important feature from the dataset [39,40]. Principal component analysis (PCA) was used to reduce the dimension of the dataset before applying the predictive model to obtain the best score [41]. Finally, the HHCA dataset was normalized before feature engineering to make it within ranges for better results.

Correlation Score with Ranker Method
The correlation score of each feature was determined with the help of the Ranker method in Weka [42]. The value for the correlation was 1 to −1. A more positive correlation means that the variables are highly correlated with the target class, and a negative correlation means they have an imperfect correlation with each other. If the correlation value is 0, then there is no correlation between the variables. Moreover, sometimes, the correlation values became negative, which means that the variables are negatively correlated, which is very bad. In the table on using Weka, the correlation score of each feature is shown concerning the target variable. This method chooses the important variables highlighted in Table 2 because the correlation value is 0 and goes towards negative after that. Therefore, we left these variables because they were not helpful.

. CFS Subset Eval Using Greedy Stepwise
The forward selection of greedy stepwise was considered here for the subset evaluation of the variable's selection. This approach is also helpful in uncovering the most influential variables from the dataset [43]. The ranking with CFS is presented in Table 3. Using this approach, a total of "12" features were selected, as listed above. This analysis only selected the features with the help of using the forward selection.

Relief F Attribute Eval with Ranker
This method also shows the correlation score of the features, and the results are given in Table 4. This filter chooses the total number of "37" features out of "40", as shown in Table 4. The correlation score for each feature is also listed to explain its importance for the "Q_p_c_s_r" target class. Principal component analysis (PCA) was beneficial to deal with the dimensionality reduction of the dataset [41]. It is a dimension-reducing technique utilized to minimize the size of large volumes of data by converting a considerable number of variables into yet another smaller one, which also retains most details in the more extensive collection [44]. We utilized PCA for the HHCA dataset, and its results are shown in Table 5. The total, "27" dimensions were used here, depending on the threshold values. Here, we set the default value confidence interval up to 95%, which is the most appropriate for the analysis. We also set the threshold value following our own needs.

Selecting Feature Selection Algorithm
In this case study, "4" feature selection algorithms were applied in the HHCA dataset to find the best features among them and remove the unwanted or uncorrelated features. Each of the algorithms works to remove several features using their threshold values. Now, selecting one feature is important for approaching further. After applying the feature selection algorithms, the datasets were saved for comparing the capabilities of the different techniques. Therefore, after applying four feature evaluation techniques, we obtained four datasets: Correlation Attribute Evaluation Dataset (CAED), Principal Component Analysis Dataset (PCAD), Relief-F Attribute Evaluation Dataset (RAED), and CFS Subset Evaluation Dataset (CSED). We selected Random Forest (RF) as the baseline classifier to check the capabilities. The performances were checked for binary classification and multiclass classification. A broad discussion on binary and multi-class classification is given in the later sections. Table 6 shows the RF classification results for binary classification and Table 7 shows them for multi-class classification. Here, a clear winner is the CFS subset evaluation dataset, where the feature was reduced from 40 to 12, a massive selection, yet scoring best among the other selection criteria. Relief F feature selection performed rather close, but in terms of reducing the features, CFS decreased to 28 features, whereas Relief F reduced to only 3. Table 8 shows the time taken by both datasets, and here, there was almost a 50% time complexity reduction for CFS subset evaluation. Therefore, there was a huge time gap, and the CFS subset evaluation also increased the model accuracy. Further binary and multi-class classification processing was performed on the CFS subset evaluation dataset.

HHCA Predictive Analysis
In this subsection, four ML models, including the Deep Neural Network (DNN), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), were implemented on the HHCA dataset. Two types of classification models (binary and multi-class) were implemented to build an effective framework for HHCAs. For this purpose, Weka tools were utilized to make the predictive models for binary and multi-class classification problems. Both approaches have their significance due to their pros and cons. The main objective of the predictive analysis is to find the most robust classifier for the HHCAs that gives the best results. The outperforming ML models for binary and multi-class classification problems were assessed using the four evaluation metrics (accuracy, precision, recall, and F-1 score). The experimental details of both techniques are presented below.

Binary Classification
This part of the research was based on the binary classification data of HHCAs. The data were labeled with only two classes, "Good" and "Bad". As we employed only two classes of data to build the ML model, this is why the part was declared as a "binary classification" approach. Various ML models were tested and trained using 10-fold crossvalidation. We used renowned models to depict high accuracy, precision, recall, and F-1 measure results. Table 9 below is clear evidence of the results we recorded using the Weka tool.  Table 9 illustrates that the Decision Tree gave low results in terms of the evaluation metrics, where the accuracy, precision, recall, and F-1 measure were 94.3%, 94.3%, 94.3%, and 94.3%, respectively. However, the Deep Neural Network model gave the best results covering the same metrics and having values of 97.4%, 97.4%, 97.4%, and 97.4%, respectively. Thus, the Deep Neural Network outperformed all other applied models. Moreover, in terms of HHCA binary class data, we were the first to explore the results using the Deep Neural Network model, giving valid results compared with all other applied models.

Multi-Class Classification
This section of the paper is based on the multi-class classification data of HHCAs. The data were labeled with five different classes, which were "Best", "Good", "Average", "Bad", and "Very Bad". As we utilized only multiple classes (five different classes) of data for building the ML model, the part was declared as a "multi-class classification" approach. Various ML models were tested and trained using 10-fold cross-validation. We used renowned models to depict the high accuracy, precision, recall, and F-1 measure results. Table 10 below is clear evidence of the effects we recorded using the Weka tool.

Comparative Analysis Using the ROC
In this case, we evaluated the ROC results for binary and multi-class classification. As we know, the binary class records have the labels "Good" and "Bad"; therefore, the outcome of the classifier is shown in Figure 2. We know that the Deep Neural Network gave outperforming binary class results (as discussed in Table 9). Therefore, we used the Deep Neural Network to obtain the results of the outcome. We employed the same technique of 10-fold cross-validation here using Weka.     There are five different classes in the multi-class approach, and we evaluated five different ROCs for each class, illustrated in Figure 3. As Table 10 describes, the Random Forest model was the best among all multi-class classification models; therefore, we used the same model. Ten-fold cross-validation was used to achieve the results. Figure 3 presents that each class had different ROC results. We evaluated that "Best", "Good", "Average", "Bad", and "Very Bad" has ROC values 99.61%, 98.83%, 98.70%, 99.18%, and 99.70%, respectively.

Discussion and Conclusions
This research proposed a solution for the Medicare industry to uncover the most influential attributes using the star ratings. Two types of ML techniques were implemented, which included binary and multi-class classification. The HHCA dataset was utilized here, which contains 70 features along with the target variable (star rating). The data pre-processing was performed along with handling missing values, removal of inconsistencies, and elimination of outliers from the dataset. Afterward, feature engineering was conducted using Weka, and four different attribute selection filters (CAE, RA, PCA, CSE) were applied to locate the most impactful attributes for binary and multi-class classification problems. The RF model was chosen for these "4" filters to find the best feature selection technique, and the findings depicted that the CSE filter had the best performance for both techniques (binary, multi-class) using the "12" features. The name of the important factors are O_H_H_A_Ser, h_t_p_c_t_m, r_c_f_s, f_c_t_p_f_c, b_m_a, h_g_o_b, g_ b_a_b, p_b_i, i_h_a_o, a_d_c_b_m, t_b_a_t_h, and P_iS_a. After selecting the best from the 70 available attributes, four renowned machine learning models (DT, SVM, DNN, RF) were utilized for binary and multi-class classification using the "12" features. The ML model's performance was analyzed based on four evaluation metrics: accuracy, precision, recall, F-1 score. For better results, the hyperparameters' tunings were also considered, and we tested the models with the best hyperparameters. The findings showed that the DNN and RF models outperformed and achieved the highest score among all other models for binary and multi-class classification, respectively. The ROC was also a significant evaluation metric for finding the significance of the model. We computed the ROC results for each class in both binary and multi-class classification. The findings of this research are helpful in the healthcare domain to improve the customer experience using the influential features for better results.
The twelve significant extracted features affecting Medicare's star rating had the following features: Offers Home Health Aide Services, how often the home health team began their patients' care in a timely manner, how often the home health team determined whether patients received a flu shot for the current flu season, How frequently did the home health care staff follow the doctor's recommendations, provide foot care, and provide education to people with diabetes, Frequency with which patients improved in their capacity to walk and move, Rate with which patients improved their ability to get out and back into bed, How frequently patients improved their ability to shower, Note on the frequency with which patients' breath improves, Rate at which surgical incisions healed or improved, Rates at which patients improved their oral medication adherence, Frequency with which home health care recipients were hospital admission, PPR Risk-Standardized Rate (Upper Limit).
Initiation of Care at the Appropriate Time was one of the important features, h_t_p_c_t_m, that was yielded in this study, and it affects the Medicare star rating. It shows the percentage of home health quality occurrences when treatment began or resumed. Timely Initiation of Care [45] came first and the feature "Offers Home Health Aide Services", O_H_H_A_Ser. This is a vital feature especially for those with disabilities. However, further analysis is needed to improve this service as Medicare does not cover home health personal care aides as a stand-alone service. It only pays for a home health personal care aide when an individual also receives skilled nursing care or rehabilitation services through home health.
Appropriate timing is one of the important aspects preventing costly rehospitalizations and improving patient outcomes. When home healthcare was delayed after hospital discharge, the patients were more likely to experience a 30-day rehospitalization, and the association between it and the rehospitalization of diabetes patients was investigated in [46], while influenza immunization received for the current flu season is another important feature, r_c_f_s, considering that influenza has been linked to 12,000 to 56,000 deaths in the United States alone each year and that geriatric adults, those ≥65 years old, are the most vulnerable to severe infection and account for up to 85% of these deaths [47]. Improvement in bathing, g_b_a_b, was another feature that was yielded in this investigation that affects the star rating. The percentage of home health quality episodes in which the patient improved his/her ability to bathe himself/herself on his/her own is reported by the "Improvement in Bathing [45]" feature. In [48], the effect of this feature was investigated to understand how alterations in the physical capacity of an older adult affect his/her preferences in bathing, as well as how the care environment incorporates these alterations.
Another aspect of this study's findings that impacts the star rating was the "Improvement in the status of surgical wounds", i_h_a_o. Home care wound management was tested in [49] to see if oral antibiotics and the wound and patient variables affected the efficacy and surgical site infection rate compared to hospital-based wound management.

Future Work
The future work of this research is a feasible guideline for researchers. The data specialist can bring valuable insights from the available data residing on the website (cms.gov accessed on: 16 March 2022). In the dataset, a few columns are text-based, which we did not consider for this research. These could be considered for future analysis. Moreover, for future studies, some of the columns were removed because of the many missing values. These missing values can be handled by employing regression techniques for building the solution (Meeyai, 2016). Thus, significant features can be discovered by considering the neglected parameters. Modern machine learning models are very sophisticated and favorable to adopt by the agencies. Thus, deep learning and ensemble models can be helpful to optimize the performance of models to obtain the optimal results.  Data Availability Statement: We declare that the data considered for this research are original and were collected by the authors for gaining insights. Moreover, the data mining and ML tools considered for this research are freely available, and we built the models in accordance with our own scenario.