COVID-19 Prediction Using Machine Learning ^†

Abstract

The COVID-19 virus caused unprecedented global disruption. There have been millions of cases and deaths reported worldwide. Accurate prediction of COVID-19 trends is crucial for effective decision-making, resource allocation, and policy formulation. ML has been shown to be an excellent method for projecting the virus’s growth and impact as it can analyze vast datasets, discover trends, and develop predictive models. This study examines the use of various machine learning techniques for the prediction of COVID-19 such as time series analysis, regression models, and classification techniques. This paper further addresses the problems and constraints of applying the ML model to this context and suggests possible enhancements for future forecasting endeavors. The overall intention of this work is to enlighten people as to how this ML-based method contributes to pandemic forecasting in terms of improvements in pandemic preparation and response schemes.

1. Introduction

COVID-19 posed a challenge for most health systems around the world, along with its huge impact on worldwide economies and routines [1]. The fast spread of the virus has spurred governments and healthcare groups to implement emergency measures aimed at controlling transmission. One of the critical aspects in managing the pandemic has been predicting the future trajectory of the virus such as forecasting infection rates, hospitalizations, and deaths [2]. With early and accurate prediction, healthcare policy decisions, resource allocation, and public health interventions can be made and carried out, potentially saving lives and reducing the burden on health care systems. Machine learning (ML) has often been an effective tool in many areas, and its promising application has been seen everywhere. With the help of large amounts of historical and real-time data, ML(machine Learning) algorithms find hidden patterns and trends that traditional models may not have understood. In the context of COVID-19, these algorithms may use a wide variety of data sources ranging from case counts and mobility data to healthcare system capacity and even social distancing behaviors to predict future outbreaks and identify potential risk factors [3,4]. This research seeks to explore how several machine learning algorithms can be used to forecast COVID-19 and its spread, as well as the outcomes.

2. Literature Review

Past research explores the application of machine learning algorithms for forecasting the probability of mortality among COVID-19 hospitalized patients based on clinical data obtained upon admission. Data from 1500 patients were used. The random forest (RF) algorithm performed the best in terms of accuracy (95.03%) and precision (94.23%). These works underline the potential of ML models, particularly RF, for helping to identify high-risk patients and optimizing hospital resources for better patient care [1]. Limitations include the retrospective approach, single-center data and imbalanced datasets used. The studies develop automatic COVID-19 prediction systems based on different techniques, such as machine learning combined with explainable AI, used to increase a model’s interpretability. The authors used an open-source dataset with over 2.7 million records; data preprocessing using various techniques such as Synthetic Minority Oversampling Technique (SMOTE) for handling imbalanced datasets, logistic regression, decision trees, and random forests; and a hybrid Convolutional Neural Network-Long ShortTerm Memory (CNN-LSTM). Among them, the CNN-LSTM obtained the highest accuracy at 96.34% and F1 Score at 0.98 [2]. It displays a machine learning model for diagnosing COVID-19 infection using simple binary characteristics including age, gender, known contact with an infected person, and primary signs of infection (for example, cough, fever, etc.). Using a database containing 51,000 training records and nearly 47,000 testing records, the suggested model had impressive accuracy (auROC of 0.90). Screening strategies should be used to manage healthcare resources well, especially for resource-limited settings [3]. The studies focused on supervised and unsupervised learning techniques. Supervised learning outperformed unsupervised methods, achieving up to 92.9% accuracy on tasks such as classification. The three most popular machine learning algorithms—logistic regression, Artificial Neural Network (ANNs), and Convolution Neural Ntwork (CNNs)have been applied to the evaluation of datasets like patient records and chest X-rays. These strategies demonstrated promise for improving diagnostic accuracy, predicting patient outcomes, and optimizing resource allocation [4]. This led to the development of a new way of predicting cases and deaths caused by COVID-19 by making use of cutting-edge deep learning and reinforcement learning methods. In one study, an Multilayer LSTM (MLSTM) model was used for predicting future trends of cases while integrating DRL for optimizing predictions based on patient symptoms. To validate the model, real-world data from India were used to obtain high performance in comparison with standard methods such as logistic regression and basic Long Short Term Memory(LSTM) models. The system has achieved high accuracy and low error rates with a strong correlation between the actual and predicted data [5]. It examines the use of machine learning in full blood count testing for the early diagnosis of COVID-19.

Algorithms were used to analyze the data from the tests. One study provided an alternative that was faster, cheaper, and easier than standard techniques like Reverse Transcription-Polymerase Chain Reaction (RT-PCR), which are time-consuming, expensive, and incorrect sometimes. The most accurate training performances were found in random forest, kStar, and K-Nearest Neighbors (KNN), and OneR had the highest testing accuracy compared to the others [6]. The study examined the full range of machine learning techniques for COVID-19 diagnosis mortality prediction and severity by using clinical and laboratory data. Support vector machines, logistic regression, and random forest are examples of supervised learning algorithms that seem to often be employed in machine learning. ML provides faster, cost-effective alternatives to standard methods of diagnosis such as RT-PCR, and they help to identify critical cases early on. Through targeting resource use towards high-risk groups, models can have huge implications for improving healthcare during pandemics [7]. The supervised machine learning techniques that can be used to forecast the existence of COVID-19 were evaluated based on a publicly available dataset. In this context, the authors used the Waikato Environment for Knowledge Analysis (WEKA) software J48 Decision Tree, random forest (RF), support vector machines (SVM), K-NN, and Naïve bayes (NB) algorithms and applied 10-fold cross-validation for model performance. SVM was found to be the best algorithm as it achieved the lowest mean absolute error of 0.012 and a maximum accuracy of 98.81% [8]. The application of machine learning for forecasting and prediction is discussed in the study, identifying infections using symptom-based models and time series analysis. It evaluates the effectiveness of various machine learning algorithms, such as the Extra Trees Classifier (ETC) and Auto Regressive Integrated Moving Average (ARIMA), in detecting infections and estimating case counts. The ETC had a prediction accuracy of 93.62%, whereas ARIMA anticipated confirmed cases. Another research paper describes the application of machine learning in healthcare for diagnosis automation, resource planning enhancement, and pandemic management. It finds that while the accuracy of ML-based forecasting is not accurate, it does provide some insight for proactive decision-making and suggests the addition of other aspects to produce better results [9]. The paper discusses COVID-19 cases around the world and within individual countries. The decision tree and linear regression algorithms predicted the time series for confirmed cases, recoveries, and deaths. The prediction accuracy was high, with an overall R² of 0.99 for confirmed instances. COVID-19 infections were expected to fall dramatically worldwide by the first week of September 2021, with a gradual halt soon after. Another study emphasizes the effect of public health interventions such as lockdowns in flattening the curve and expands the model to assess the influence of health guidelines [10]. Using blood biomarkers, the study develops a machine learning model to forecast mortality risk in COVID-19 patients. The researchers examined data from 398 individuals, including 43 deaths, and focused on 5 critical biomarkers. For forecasting death up to 48 h in advance, a support vector machine (SVM) model demonstrated 91% sensitivity and specificity (AUC 0.93). The model was interpreted using Shapley additive explanations (SHAPs), which revealed that inflammatory markers and renal function indicators played major roles in mortality prediction. In another study, the results highlight the approach guiding clinical decisions and optimizing resource allocation in critical care [11]. The research aims at studying the usage of machine learning classifiers [12,13,14] to predict COVID-19 infection using 14 clinical characteristics. In this paper, data was from machine learning classifiers predicting COVID-19 infection using 14 clinical characteristics. In this paper, data was also sourced through auto ML. The analysis used data from 4313 patients and 48 clinical variables, and the top model was a stacked ensemble with an Area Under the Precision-Recall Curve (AUPRC) of 0.807. The data were then used to predict the COVID-19. A retrained model with these ten variables achieved comparable results, with AUPRC = 0.791 [15]. Existing schemes and their performance are summarized in

Table 1. Existing schemes.

Paper	Model Used	Accuracy/Performance
[1]	Random Forest (RF)	95.03% (Precision: 94.23%)
[2]	CNN-LSTM, Random Forest, Logistic Regression, Decision Trees	CNN-LSTM: 96.34% (F1 Score: 0.98)
[3]	ML Model using Binary Features (Age, Gender, Symptoms)	AUROC: 0.90
[4]	Supervised Learning (Logistic Regression, ANN, CNN)	Up to 92.9% accuracy
[5]	MLSTM + Deep Reinforcement Learning (DRL)	High correlation, low error rates

.

3. Methodology

3.1. Data Collection and Preprocessing

The dataset has 58,561 samples with 9 columns for each sample, providing relevant information on what would affect the outcome or course of COVID-19. All these attributes consist of demographics, comorbidities, symptoms, and a history of exposure, included to potentially generate worthwhile outcomes about COVID-19 infection and severity. Before analysis, categorical variables were encoded into numbers by ensuring uniform data transformation. All the numerical features were standardized, thus standardizing their values to have a mean of zero and a standard deviation of one, thus allowing for comparison during model training. Missing values were handled by imputing appropriate replacements for them [16].

3.2. Split Dataset

During the data split step, the dataset was divided into two sets: 30% was used for testing and the leftover 70% was used for training [17]. This partitioning allowed for machine learning model training on the majority of the data, resulting in accurate performance evaluation on the reserved test set.

3.3. Feature Selection

A random forest model was used to analyze and rank the importance of various features for predicting COVID-19. This analysis revealed several significant predictors that play a crucial role in the outcomes [18]. These factors were identified as key contributors that may influence the overall results and further investigation.

3.4. Model Development

Several machine learning techniques were used in this investigation to evaluate how well they performed on the dataset:

• Logistic Regression: Here, the baseline model applied was logistic regression, which gave a standard model against which all other models would be compared. Logistic regression is mostly used for the binary classification.
• K-Nearest Neighbors (KNN): The technique was applied with different values of k, specifically 1 and 3, to see which value of k gave the optimal number of nearest neighbors to classify the data points. KNN is a nonparametric method that can easily adapt to any kind of data distribution.
• Random Forest: This is an ensemble learning method, which is suitable for tuning with GridSearchCV, systematically searching through multiple combinations of hyperparameters to discover the best configuration for improving accuracy and performance.
• Decision Tree: This is a nonparametric model that does not assume any particular distribution of data.
• Naïve Bayes: This is a parametric model that assumes conditional independence between features.

3.5. Ensemble Method

A soft voting ensemble extends the benefits of logistic regression, random forest, KNN, and Naive Bayes for improving model robustness. Combining the respective strengths of these four algorithms for predictions, it leverages these predictions to become more accurate and reliable in their findings, and this is precisely where it will be very effective with complex data. This methodology covers five steps, which include Data Preprocessing, Split Data, Feature Selection, Model Development and Ensemble methods.

4. Results and Discussion

Table 2. Evaluation for each model.

Model	Accuracy	Precision	Recall
Logistic Regression	73.00%	77.28%	67.82%
KNN (k = 1)	63.00%	65.56%	58.08%
KNN (k = 3)	61.67%	63.85%	59.36%
Random Forest	79.33%	81.59%	71.67%
SVM	87.00%	91.54%	79.15%
Naive Bayes	49.33%	51.61%	41.03%
Decision Tree	59.93%	51.49%	44.23%
Ensemble (Soft)	51.70%	53.20%	49.85%

summarizes the evaluation for each model. SVM achieved the highest accuracy at 87.00%, logistic regression, KNN performed quite well. Still, these results suggest areas for improvement, specifically with regard to sensitivity and false positive detection. The workflow for applying the K-Nearest Neighbors (KNN) model using RapidMiner is illustrated in Figure 1.

5. Conclusions

The development of a machine learning (ML) model that can accurately forecast COVID-19 outcomes has advanced significantly as a result of this effort. Through training and evaluation, we have proved the potential usefulness of our method. Our findings emphasized the capabilities of several algorithms among the models studied. However, we acknowledge the need for additional improvements, particularly in resolving sensitivity and class imbalance issues to ensure consistent predictions across diverse patient profiles. In the future, we will concentrate on several key areas:

• Increasing the size of datasets to better understand the factors affecting COVID-19 outcomes in different groups.
• We will perform hyperparameter optimization and feature selection to improve model performance and reduce mistakes.
• Class Imbalance: We will use more advanced techniques such as oversampling, under sampling, and algorithmic tuning to make the model more robust and accurate for under-represented groups. Our findings have wide-ranging implications for the advancement of better care for patients. This work serves as foundation upon which more powerful diagnostic tools can be developed that will allow practitioners to make more informed decisions with accurate, data-driven predictions.