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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 55936

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Dr. Giovanna Castellano

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Department of Computer Science, University of Bari Aldo Moro, Via Orabona, 4-70125 Bari, Italy
Interests: image processing; computer vision; fuzzy systems; fuzzy clustering; image retrieval; neural networks; neuro-fuzzy modeling; granular computing; recommender systems
Special Issues, Collections and Topics in MDPI journals

Dr. Gabriella Casalino

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Guest Editor

Department of Computer Science, University of Bari Aldo Moro, Via Orabona, 4-70125 Bari, Italy
Interests: computational intelligence; knowledge discovery from data; intelligent data analysis; matrix factorizations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The amount of patient health data has been estimated to reach 2314 Exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such vast amounts of data; thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast amounts of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data and processes. The use of CI in healthcare can improve the management of clinical disease by introducing intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well for analysis of administrative processes.

This Special Issue will publish original research, overviews, and applications of CI methods for Healthcare. Areas of interest include, but are not limited to the following:

Fuzzy logic and fuzzy models for healthcare
Evolutionary computing for healthcare
Artificial neural networks for healthcare
Probabilistic models for healthcare
CI and Big data in healthcare
Data mining in healthcare
CI applications in healthcare

Prof. Giovanna Castellano
Dr. Gabriella Casalino
Guest Editors

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Keywords

Computational intelligence
Soft computing
Medical diagnosis
e-Health

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Published Papers (13 papers)

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Editorial

Jump to: Research

4 pages, 163 KiB

Open AccessEditorial

Special Issue on Computational Intelligence for Healthcare

by Gabriella Casalino and Giovanna Castellano

Electronics 2021, 10(15), 1841; https://doi.org/10.3390/electronics10151841 - 31 Jul 2021

Viewed by 1469

Abstract

The number of patient health data has been estimated to have reached 2314 exabytes by 2020 [...] Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

Research

Jump to: Editorial

13 pages, 1494 KiB

Open AccessFeature PaperArticle

Explaining Ovarian Cancer Gene Expression Profiles with Fuzzy Rules and Genetic Algorithms

by Arianna Consiglio, Gabriella Casalino, Giovanna Castellano, Giorgio Grillo, Elda Perlino, Gennaro Vessio and Flavio Licciulli

Electronics 2021, 10(4), 375; https://doi.org/10.3390/electronics10040375 - 4 Feb 2021

Cited by 10 | Viewed by 2239

Abstract

The analysis of gene expression data is a complex task, and many tools and pipelines are available to handle big sequencing datasets for case-control (bivariate) studies. In some cases, such as pilot or exploratory studies, the researcher needs to compare more than two groups of samples consisting of a few replicates. Both standard statistical bioinformatic pipelines and innovative deep learning models are unsuitable for extracting interpretable patterns and information from such datasets. In this work, we apply a combination of fuzzy rule systems and genetic algorithms to analyze a dataset composed of 21 samples and 6 classes, useful for approaching the study of expression profiles in ovarian cancer, compared to other ovarian diseases. The proposed method is capable of performing a feature selection among genes that is guided by the genetic algorithm, and of building a set of if-then rules that explain how classes can be distinguished by observing changes in the expression of selected genes. After testing several parameters, the final model consists of 10 genes involved in the molecular pathways of cancer and 10 rules that correctly classify all samples. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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16 pages, 1219 KiB

Open AccessArticle

An Ensemble Learning Approach Based on Diffusion Tensor Imaging Measures for Alzheimer’s Disease Classification

by Eufemia Lella, Andrea Pazienza, Domenico Lofù, Roberto Anglani and Felice Vitulano

Electronics 2021, 10(3), 249; https://doi.org/10.3390/electronics10030249 - 22 Jan 2021

Cited by 22 | Viewed by 4348

Abstract

Recent advances in neuroimaging techniques, such as diffusion tensor imaging (DTI), represent a crucial resource for structural brain analysis and allow the identification of alterations related to severe neurodegenerative disorders, such as Alzheimer’s disease (AD). At the same time, machine-learning-based computational tools for early diagnosis and decision support systems are adopted to uncover hidden patterns in data for phenotype stratification and to identify pathological scenarios. In this landscape, ensemble learning approaches, conceived to simulate human behavior in making decisions, are suitable methods in healthcare prediction tasks, generally improving classification performances. In this work, we propose a novel technique for the automatic discrimination between healthy controls and AD patients, using DTI measures as predicting features and a soft-voting ensemble approach for the classification. We show that this approach, efficiently combining single classifiers trained on specific groups of features, is able to improve classification performances with respect to the comprehensive approach of the concatenation of global features (with an increase of up to

9 %

on average) and the use of individual groups of features (with a notable enhancement in sensitivity of up to

11 %

). Ultimately, the feature selection phase in similar classification tasks can take advantage of this kind of strategy, allowing one to exploit the information content of data and at the same time reducing the dimensionality of the feature space, and in turn the computational effort. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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32 pages, 11655 KiB

Open AccessArticle

Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection

by Muhammad Ijaz, Gang Li, Huiquan Wang, Ahmed M. El-Sherbeeny, Yussif Moro Awelisah, Ling Lin, Anis Koubaa and Alam Noor

Electronics 2020, 9(12), 2015; https://doi.org/10.3390/electronics9122015 - 28 Nov 2020

Cited by 20 | Viewed by 3883

Abstract

Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%). Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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13 pages, 985 KiB

Open AccessArticle

Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis

by Sarah A. Ebiaredoh-Mienye, Ebenezer Esenogho and Theo G. Swart

Electronics 2020, 9(11), 1963; https://doi.org/10.3390/electronics9111963 - 20 Nov 2020

Cited by 28 | Viewed by 3106

Abstract

In recent times, several machine learning models have been built to aid in the prediction of diverse diseases and to minimize diagnostic errors made by clinicians. However, since most medical datasets seem to be imbalanced, conventional machine learning algorithms tend to underperform when trained with such data, especially in the prediction of the minority class. To address this challenge and proffer a robust model for the prediction of diseases, this paper introduces an approach that comprises of feature learning and classification stages that integrate an enhanced sparse autoencoder (SAE) and Softmax regression, respectively. In the SAE network, sparsity is achieved by penalizing the weights of the network, unlike conventional SAEs that penalize the activations within the hidden layers. For the classification task, the Softmax classifier is further optimized to achieve excellent performance. Hence, the proposed approach has the advantage of effective feature learning and robust classification performance. When employed for the prediction of three diseases, the proposed method obtained test accuracies of 98%, 97%, and 91% for chronic kidney disease, cervical cancer, and heart disease, respectively, which shows superior performance compared to other machine learning algorithms. The proposed approach also achieves comparable performance with other methods available in the recent literature. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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19 pages, 3140 KiB

Open AccessArticle

Searching for Premature Ventricular Contraction from Electrocardiogram by Using One-Dimensional Convolutional Neural Network

by Junsheng Yu, Xiangqing Wang, Xiaodong Chen and Jinglin Guo

Electronics 2020, 9(11), 1790; https://doi.org/10.3390/electronics9111790 - 28 Oct 2020

Cited by 4 | Viewed by 4549

Abstract

Premature ventricular contraction (PVC) is a common cardiac arrhythmia that can occur in ordinary healthy people and various heart disease patients. Clinically, cardiologists usually use a long-term electrocardiogram (ECG) as a medium to detect PVC. However, it is time-consuming and labor-intensive for cardiologists to analyze the long-term ECG accurately. To this end, this paper suggests a simple but effective approach to search for PVC from the long-term ECG. The recommended method first extracts each heartbeat from the long-term ECG by applying a fixed time window. Subsequently, the model based on the one-dimensional convolutional neural network (CNN) tags these heartbeats without any preprocessing, such as denoise. Unlike previous PVC detection methods that use hand-crafted features, the proposed plan rationally and automatically extracts features and identify PVC with supervised learning. The proposed PVC detection algorithm acquires 99.64% accuracy, 96.97% sensitivity, and 99.84% specificity for the MIT-BIH arrhythmia database. Besides, when the number of samples in the training set is 3.3 times that of the test set, the proposed method does not misjudge any heartbeat from the test set. The simulation results show that it is reliable to use one-dimensional CNN for PVC recognition. More importantly, the overall system does not rely on complex and cumbersome preprocessing. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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14 pages, 254 KiB

Open AccessArticle

Two-Stage Monitoring of Patients in Intensive Care Unit for Sepsis Prediction Using Non-Overfitted Machine Learning Models

by Vytautas Abromavičius, Darius Plonis, Deividas Tarasevičius and Artūras Serackis

Electronics 2020, 9(7), 1133; https://doi.org/10.3390/electronics9071133 - 12 Jul 2020

Cited by 21 | Viewed by 3707

Abstract

The presented research faces the problem of early detection of sepsis for patients in the Intensive Care Unit. The PhysioNet/Computing in Cardiology Challenge 2019 facilitated the development of automated, open-source algorithms for the early detection of sepsis from clinical data. A labeled clinical records dataset for training and verification of the algorithms was provided by the challenge organizers. However, a relatively small number of records with sepsis, supported by Sepsis-3 clinical criteria, led to highly unbalanced dataset (only 2% records with sepsis label). A high number of unbalanced data records is a great challenge for machine learning model training and is not suitable for training classical classifiers. To address these issues, a method taking into the account the amount of time the patients spent in the intensive care unit (ICU) was proposed. The proposed method uses two separate ensemble models, one trained on patient records under 56 h in the ICU, and another for patients who stayed longer than 56 h. A solution including feature selection and weighting based training on imbalanced data was proposed in this paper. In addition, several performance metrics were investigated. Results show, that for successful prediction, a particular model having few or more predictors based on the length of stay in the Intensive Care Unit should be applied. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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20 pages, 3746 KiB

Open AccessFeature PaperArticle

A Synchronized Multi-Unit Wireless Platform for Long-Term Activity Monitoring

by Giuseppe Coviello, Gianfranco Avitabile and Antonello Florio

Electronics 2020, 9(7), 1118; https://doi.org/10.3390/electronics9071118 - 10 Jul 2020

Cited by 30 | Viewed by 3880

Abstract

One of the objectives of the medicine is to modify patients’ ways of living. In this context, a key role is played by the diagnosis. When dealing with acquisition systems consisting of multiple wireless devices located in different parts of the body, it becomes fundamental to ensure synchronization between the individual units. This task is truly a challenge, so one aims to limit the complexity of the calculation and ensure long periods of operation. In fact, in the absence of synchronization, it is impossible to relate all the measurements coming from the different subsystems on a single time scale for the extraction of complex characteristics. In this paper, we first analyze in detail all the possible causes that lead to have a system that is not synchronous and therefore not usable. Then, we propose a firmware implementation strategy and a simple but effective protocol that guarantees perfect synchrony between the devices while keeping computational complexity low. The employed network has a star topology with a master/slave architecture. In this paper a new approach to the synchronization problem is introduced to guarantee a precise but not necessarily accurate synchronization between the units. In order to demonstrate the effectiveness of the proposed solution, a platform consisting of two different types of units has been designed and built. In particular, a nine Degrees of Freedom (DoF) Inertial Measurement Unit (IMU) is used in one unit while a nine-DoF IMU and all circuits for the analysis of the superficial Electromyography (sEMG) are present on the other unit. The system is completed by an Android app that acts as a user interface for starting and stopping the logging operations. The paper experimentally demonstrates that the proposed solution overcomes all the limits set out and it guarantees perfect synchronization of the single measurement, even during long-duration acquisitions. In fact, a less than 30

μ

s time mismatch has been registered for a 24 h test, and the possibility to perform complex post-processing on the acquired data with a simple and effective system has been proven. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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16 pages, 1377 KiB

Open AccessArticle

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

by Jyostna Devi Bodapati, Veeranjaneyulu Naralasetti, Shaik Nagur Shareef, Saqib Hakak, Muhammad Bilal, Praveen Kumar Reddy Maddikunta and Ohyun Jo

Electronics 2020, 9(6), 914; https://doi.org/10.3390/electronics9060914 - 30 May 2020

Cited by 122 | Viewed by 5535

Abstract

Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. The major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. To extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extracts different features, fusing them using 1D pooling and cross pooling leads to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that DNN with dropout at input layer converges more quickly when trained using blended features, compared to the same model trained using uni-modal deep features. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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15 pages, 2797 KiB

Open AccessArticle

Realizing an Integrated Multistage Support Vector Machine Model for Augmented Recognition of Unipolar Depression

by Kathiravan Srinivasan, Nivedhitha Mahendran, Durai Raj Vincent, Chuan-Yu Chang and Shabbir Syed-Abdul

Electronics 2020, 9(4), 647; https://doi.org/10.3390/electronics9040647 - 15 Apr 2020

Cited by 16 | Viewed by 2925

Abstract

Unipolar depression (UD), also referred to as clinical depression, appears to be a widespread mental disorder around the world. Further, this is a vital state related to a person’s health that influences his/her daily routine. Besides, this state also influences the person’s frame of mind, behavior, and several body functionalities like sleep, appetite, and also it can cause a scenario where a person could harm himself/herself or others. In several cases, it becomes an arduous task to detect UD, since, it is a state of comorbidity. For that reason, this research proposes a more convenient approach for the physicians to detect the state of clinical depression at an initial phase using an integrated multistage support vector machine model. Initially, the dataset is preprocessed using multiple imputation by chained equations (MICE) technique. Then, for selecting the appropriate features, the support vector machine-based recursive feature elimination (SVM RFE) is deployed. Subsequently, the integrated multistage support vector machine classifier is built by employing the bagging random sampling technique. Finally, the experimental outcomes indicate that the proposed integrated multistage support vector machine model surpasses methods such as logistic regression, multilayer perceptron, random forest, and bagging SVM (majority voting), in terms of overall performance. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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22 pages, 4157 KiB

Open AccessArticle

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

by Hannah Inbarani H., Ahmad Taher Azar and Jothi G

Electronics 2020, 9(1), 188; https://doi.org/10.3390/electronics9010188 - 19 Jan 2020

Cited by 52 | Viewed by 5106

Abstract

Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widely used for image segmentation in medical image processing. In this article, a novel hybrid histogram-based soft covering rough k-means clustering (HSCRKM) algorithm for leukemia nucleus image segmentation is discussed. This algorithm combines the strengths of a soft covering rough set and rough k-means clustering. The histogram method was utilized to identify the number of clusters to avoid random initialization. Different types of features such as gray level co-occurrence matrix (GLCM), color, and shape-based features were extracted from the segmented image of the nucleus. Machine learning prediction algorithms were applied to classify the cancerous and non-cancerous cells. The proposed strategy is compared with an existing clustering algorithm, and the efficiency is evaluated based on the prediction metrics. The experimental results show that the HSCRKM method efficiently segments the nucleus, and it is also inferred that logistic regression and neural network perform better than other prediction algorithms. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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9 pages, 375 KiB

Open AccessFeature PaperArticle

CRISPRLearner: A Deep Learning-Based System to Predict CRISPR/Cas9 sgRNA On-Target Cleavage Efficiency

by Giovanni Dimauro, Pierpasquale Colagrande, Roberto Carlucci, Mario Ventura, Vitoantonio Bevilacqua and Danilo Caivano

Electronics 2019, 8(12), 1478; https://doi.org/10.3390/electronics8121478 - 4 Dec 2019

Cited by 22 | Viewed by 5555

Abstract

CRISPRLearner, the system presented in this paper, makes it possible to predict the on-target cleavage efficiency (also called on-target knockout efficiency) of a given sgRNA sequence, specifying the target genome that this sequence is designed for. After efficiency prediction, the researcher can evaluate its sequence and design a new one if the predicted efficiency is low. CRISPRLearner uses a deep convolutional neural network to automatically learn sequence determinants and predict the efficiency, using pre-trained models or using a model trained on a custom dataset. The convolutional neural network uses linear regression to predict efficiency based on efficiencies used to train the model. Ten different models were trained using ten different gene datasets. The efficiency prediction task attained an average Spearman correlation higher than 0.40. This result was obtained using a data augmentation technique that generates mutations of a sgRNA sequence, maintaining the efficiency value. CRISPRLearner supports researchers in sgRNA design task, predicting a sgRNA on-target knockout efficiency. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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15 pages, 3137 KiB

Open AccessFeature PaperArticle

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

by Christian Morbidoni, Alessandro Cucchiarelli, Sandro Fioretti and Francesco Di Nardo

Electronics 2019, 8(8), 894; https://doi.org/10.3390/electronics8080894 - 14 Aug 2019

Cited by 66 | Viewed by 6989

Abstract

Correctly identifying gait phases is a prerequisite to achieve a spatial/temporal characterization of muscular recruitment during walking. Recent approaches have addressed this issue by applying machine learning techniques to treadmill-walking data. We propose a deep learning approach for surface electromyographic (sEMG)-based classification of stance/swing phases and prediction of the foot–floor-contact signal in more natural walking conditions (similar to everyday walking ones), overcoming constraints of a controlled environment, such as treadmill walking. To this aim, sEMG signals were acquired from eight lower-limb muscles in about 10.000 strides from 23 healthy adults during level ground walking, following an eight-shaped path including natural deceleration, reversing, curve, and acceleration. By means of an extensive evaluation, we show that using a multi layer perceptron to learn hidden features provides state of the art performances while avoiding features engineering. Results, indeed, showed an average classification accuracy of

94.9

for learned subjects and

93.4

for unlearned ones, while mean absolute difference (

\pm S D

) between phase transitions timing predictions and footswitch data was

21.6

ms and

38.1

ms for heel-strike and toe off, respectively. The suitable performance achieved by the proposed method suggests that it could be successfully used to automatically classify gait phases and predict foot–floor-contact signal from sEMG signals during level ground walking. Full article

(This article belongs to the Special Issue Computational Intelligence in Healthcare)

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