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Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (11 March 2022) | Viewed by 43432

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Dr. Cornelio Yáñez Márquez

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Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico
Interests: deep learning; associative models; machine learning; pattern recognition; neural networks; metaheuristics
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Prof. Dr. Yenny Villuendas-Rey

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Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Ciudad de México, México
Interests: optimization; bio-inspired algorithms; machine learning; rough sets; biomedical applications
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Prof. Dr. Miltiadis D. Lytras

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Computer Science Department, College of Engineering, Effat University, Jeddah, Saudi Arabia
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
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Special Issue Information

Dear Colleagues,

The arrival of the third millennium has brought impressive developments and advances in machine learning algorithms. Recent advances in deep learning, the algorithms of which are accelerated with CUDA hardware cards, deserve special mention. The applications of this type of algorithms have permeated a wide range of human activities, including the sensitive area of health research.

The contents of high impact research journals bear witness to the efforts of scientists on such relevant topics as the classification, diagnosis and prognosis of diseases. Given the speed with which these investigations are advancing, due to the rapid development of new hardware, software and application platforms, it is necessary to promote new investigations that support physicians and health researchers.

This Special Issue seeks unpublished contributions of high scientific quality on the topic of the classification, diagnosis and prognosis of diseases using machine learning algorithms.

Dr. Cornelio Yáñez Márquez
Dr. Yenny Villuendas-Rey
Prof. Dr. Miltiadis D. Lytras
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

machine learning
classification of diseases
diagnosis of diseases
prognosis of diseases
cancer
chronic diseases
artificial intelligence
associative memories
deep learning
data mining
big data

Published Papers (12 papers)

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Research

25 pages, 5095 KiB

Open AccessArticle

Older Adults Get Lost in Virtual Reality: Visuospatial Disorder Detection in Dementia Using a Voting Approach Based on Machine Learning Algorithms

by Areej Y. Bayahya, Wadee Alhalabi and Sultan H. Alamri

Mathematics 2022, 10(12), 1953; https://0-doi-org.brum.beds.ac.uk/10.3390/math10121953 - 07 Jun 2022

Cited by 4 | Viewed by 2449

Abstract

As the age of an individual progresses, they are prone to more diseases; dementia is one of these age-related diseases. Regarding the detection of dementia, traditional cognitive testing is currently one of the most accurate tests. Nevertheless, it has many disadvantages, e.g., it does not measure the extent of the brain damage and does not take the patient’s intelligence into consideration. In addition, traditional assessment does not measure dementia under real-world conditions and in daily tasks. It is therefore advisable to investigate the newest, more powerful applications that combine cognitive techniques with computerized techniques. Virtual reality worlds are one example, and allow patients to immerse themselves in a controlled environment. This study created the Medical Visuospatial Dementia Test (referred to as the “MVD Test”) as a non-invasive, semi-immersive, and cognitive computerized test. It uses a 3D virtual environment platform based on medical tasks combined with AI algorithms. The objective is to evaluate two cognitive domains: visuospatial assessment and memory assessment. Using multiple machine learning algorithms (MLAs), based on different voting approaches, a 3D system classifies patients into three classes: patients with normal cognition, patients with mild cognitive impairment (MCI), and patients with severe cognitive impairment (dementia). The model with the highest performance was derived from voting approach named Ensemble Vote, where accuracy was 97.22%. Cross-validation accuracy of Extra Tree and Random Forest classifiers, which was greater than 99%, indicated a greater discriminate capacity than that of other classes. Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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16 pages, 2442 KiB

Open AccessArticle

Robust Detection and Modeling of the Major Temporal Arcade in Retinal Fundus Images

by Dora Elisa Alvarado-Carrillo, Iván Cruz-Aceves, Martha Alicia Hernández-González and Luis Miguel López-Montero

Mathematics 2022, 10(8), 1334; https://0-doi-org.brum.beds.ac.uk/10.3390/math10081334 - 18 Apr 2022

Cited by 2 | Viewed by 1772

Abstract

The Major Temporal Arcade (MTA) is a critical component of the retinal structure that facilitates clinical diagnosis and monitoring of various ocular pathologies. Although recent works have addressed the quantitative analysis of the MTA through parametric modeling, their efforts are strongly based on an assumption of symmetry in the MTA shape. This work presents a robust method for the detection and piecewise parametric modeling of the MTA in fundus images. The model consists of a piecewise parametric curve with the ability to consider both symmetric and asymmetric scenarios. In an initial stage, multiple models are built from random blood vessel points taken from the blood-vessel segmented retinal image, following a weighted-RANSAC strategy. To choose the final model, the algorithm extracts blood-vessel width and grayscale-intensity features and merges them to obtain a coarse MTA probability function, which is used to weight the percentage of inlier points for each model. This procedure promotes selecting a model based on points with high MTA probability. Experimental results in the public benchmark dataset Digital Retinal Images for Vessel Extraction (DRIVE), for which manual MTA delineations have been prepared, indicate that the proposed method outperforms existing approaches with a balanced Accuracy of

0.7067

, Mean Distance to Closest Point of

7.40

pixels, and Hausdorff Distance of

27.96

pixels, while demonstrating competitive results in terms of execution time (

9.93

s per image). Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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14 pages, 1726 KiB

Open AccessArticle

Enhanced Fuzzy Elephant Herding Optimization-Based OTSU Segmentation and Deep Learning for Alzheimer’s Disease Diagnosis

by Afnan M. Alhassan, The Alzheimer’s Disease Neuroimaging Initiative and The Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing

Mathematics 2022, 10(8), 1259; https://0-doi-org.brum.beds.ac.uk/10.3390/math10081259 - 11 Apr 2022

Cited by 9 | Viewed by 1877

Abstract

Several neurological illnesses and diseased sites have been studied, along with the anatomical framework of the brain, using structural MRI (sMRI). It is critical to diagnose Alzheimer’s disease (AD) patients in a timely manner to implement preventative treatments. The segmentation of brain anatomy and categorization of AD have received increased attention since they can deliver good findings spanning a vast range of information. The first research gap considered in this work is the real-time efficiency of OTSU segmentation, which is not high, despite its simplicity and good accuracy. A second issue is that feature extraction could be automated by implementing deep learning techniques. To improve picture segmentation’s real-timeliness, enhanced fuzzy elephant herding optimization (EFEHO) was used for OTSU segmentation, and named EFEHO-OTSU. The main contribution of this work is twofold. One is utilizing EFEHO in the recommended technique to seek the optimal segmentation threshold for the OTSU method. Second, dual attention multi-instance deep learning network (DA-MIDL) is recommended for the timely diagnosis of AD and its prodromal phase, mild cognitive impairment (MCI). Tests show that this technique converges faster and takes less time than the classic OTSU approach without reducing segmentation performance. This study develops a valuable tool for quick picture segmentation with good real-time efficiency. Compared to numerous conventional techniques, the suggested study attains improved categorization performance regarding accuracy and transferability. Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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21 pages, 10604 KiB

Open AccessArticle

A New Algorithm for Multivariate Genome Wide Association Studies Based on Differential Evolution and Extreme Learning Machines

by David Álvarez Gutiérrez, Fernando Sánchez Lasheras, Vicente Martín Sánchez, Sergio Luis Suárez Gómez, Víctor Moreno, Ferrán Moratalla-Navarro and Antonio José Molina de la Torre

Mathematics 2022, 10(7), 1024; https://0-doi-org.brum.beds.ac.uk/10.3390/math10071024 - 23 Mar 2022

Cited by 1 | Viewed by 1650

Abstract

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants, whose aim is to find those that are linked to a certain trait or illness. Due to the multivariate nature of these kinds of studies, machine learning methodologies have been already applied in them, showing good performance. This work presents a new methodology for GWAS that makes use of extreme learning machines and differential evolution. The proposed methodology was tested with the help of the genetic information (370,750 single-nucleotide polymorphisms) of 2049 individuals, 1076 of whom suffer from colorectal cancer. The possible relationship of 10 different pathways with this illness was tested. The results achieved showed that the proposed methodology is suitable for detecting relevant pathways for the trait under analysis with a lower computational cost than other machine learning methodologies previously proposed. Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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24 pages, 2010 KiB

Open AccessArticle

COVID-19 Detection Systems Using Deep-Learning Algorithms Based on Speech and Image Data

by Ali Bou Nassif, Ismail Shahin, Mohamed Bader, Abdelfatah Hassan and Naoufel Werghi

Mathematics 2022, 10(4), 564; https://0-doi-org.brum.beds.ac.uk/10.3390/math10040564 - 11 Feb 2022

Cited by 26 | Viewed by 3140

Abstract

The global epidemic caused by COVID-19 has had a severe impact on the health of human beings. The virus has wreaked havoc throughout the world since its declaration as a worldwide pandemic and has affected an expanding number of nations in numerous countries around the world. Recently, a substantial amount of work has been done by doctors, scientists, and many others working on the frontlines to battle the effects of the spreading virus. The integration of artificial intelligence, specifically deep- and machine-learning applications, in the health sector has contributed substantially to the fight against COVID-19 by providing a modern innovative approach for detecting, diagnosing, treating, and preventing the virus. In this proposed work, we focus mainly on the role of the speech signal and/or image processing in detecting the presence of COVID-19. Three types of experiments have been conducted, utilizing speech-based, image-based, and speech and image-based models. Long short-term memory (LSTM) has been utilized for the speech classification of the patient’s cough, voice, and breathing, obtaining an accuracy that exceeds 98%. Moreover, CNN models VGG16, VGG19, Densnet201, ResNet50, Inceptionv3, InceptionResNetV2, and Xception have been benchmarked for the classification of chest X-ray images. The VGG16 model outperforms all other CNN models, achieving an accuracy of 85.25% without fine-tuning and 89.64% after performing fine-tuning techniques. Furthermore, the speech–image-based model has been evaluated using the same seven models, attaining an accuracy of 82.22% by the InceptionResNetV2 model. Accordingly, it is inessential for the combined speech–image-based model to be employed for diagnosis purposes since the speech-based and image-based models have each shown higher terms of accuracy than the combined model. Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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19 pages, 1465 KiB

Open AccessArticle

Weighted Hybrid Feature Reduction Embedded with Ensemble Learning for Speech Data of Parkinson’s Disease

by Zeeshan Hameed, Waheed Ur Rehman, Wakeel Khan, Nasim Ullah and Fahad R. Albogamy

Mathematics 2021, 9(24), 3172; https://0-doi-org.brum.beds.ac.uk/10.3390/math9243172 - 09 Dec 2021

Cited by 3 | Viewed by 1973

Abstract

Parkinson’s disease (PD) is a progressive and long-term neurodegenerative disorder of the central nervous system. It has been studied that 90% of the PD subjects have voice impairments which are some of the vital characteristics of PD patients and have been widely used for diagnostic purposes. However, the curse of dimensionality, high aliasing, redundancy, and small sample size in PD speech data bring great challenges to classify PD objects. Feature reduction can efficiently solve these issues. However, existing feature reduction algorithms ignore high aliasing, noise, and the stability of algorithms, and thus fail to give substantial classification accuracy. To mitigate these problems, this study proposes a weighted hybrid feature reduction embedded with ensemble learning technique which comprises (1) hybrid feature reduction technique that increases inter-class variance, reduces intra-class variance, preserves the neighborhood structure of data, and remove co-related features that causes high aliasing and noise in classification. (2) Weighted-boosting method to train the model precisely. (3) Furthermore, the stability of the algorithm is enhanced by introducing a bagging strategy. The experiments were performed on three different datasets including two widely used datasets and a dataset provided by Southwest Hospital (Army Military Medical University) Chongqing, China. The experimental results indicated that compared with existing feature reduction methods, the proposed algorithm always shows the highest accuracy, precision, recall, and G-mean for speech data of PD. Moreover, the proposed algorithm not only shows excellent performance for classification but also deals with imbalanced data precisely and achieved the highest AUC in most of the cases. In addition, compared with state-of-the-art algorithms, the proposed method shows improvement up to 4.53%. In the future, this algorithm can be used for early and differential diagnoses, which are rated as challenging tasks. Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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25 pages, 3803 KiB

Open AccessArticle

Identifying the Main Risk Factors for Cardiovascular Diseases Prediction Using Machine Learning Algorithms

by Luis Rolando Guarneros-Nolasco, Nancy Aracely Cruz-Ramos, Giner Alor-Hernández, Lisbeth Rodríguez-Mazahua and José Luis Sánchez-Cervantes

Mathematics 2021, 9(20), 2537; https://0-doi-org.brum.beds.ac.uk/10.3390/math9202537 - 09 Oct 2021

Cited by 11 | Viewed by 3566

Abstract

Cardiovascular Diseases (CVDs) are a leading cause of death globally. In CVDs, the heart is unable to deliver enough blood to other body regions. As an effective and accurate diagnosis of CVDs is essential for CVD prevention and treatment, machine learning (ML) techniques can be effectively and reliably used to discern patients suffering from a CVD from those who do not suffer from any heart condition. Namely, machine learning algorithms (MLAs) play a key role in the diagnosis of CVDs through predictive models that allow us to identify the main risks factors influencing CVD development. In this study, we analyze the performance of ten MLAs on two datasets for CVD prediction and two for CVD diagnosis. Algorithm performance is analyzed on top-two and top-four dataset attributes/features with respect to five performance metrics –accuracy, precision, recall, f1-score, and roc-auc—using the train-test split technique and k-fold cross-validation. Our study identifies the top-two and top-four attributes from CVD datasets analyzing the performance of the accuracy metrics to determine that they are the best for predicting and diagnosing CVD. As our main findings, the ten ML classifiers exhibited appropriate diagnosis in classification and predictive performance with accuracy metric with top-two attributes, identifying three main attributes for diagnosis and prediction of a CVD such as arrhythmia and tachycardia; hence, they can be successfully implemented for improving current CVD diagnosis efforts and help patients around the world, especially in regions where medical staff is lacking. Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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13 pages, 1874 KiB

Open AccessArticle

Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance

by Gustavo A. Alonso-Silverio, Víctor Francisco-García, Iris P. Guzmán-Guzmán, Elías Ventura-Molina and Antonio Alarcón-Paredes

Mathematics 2021, 9(20), 2529; https://0-doi-org.brum.beds.ac.uk/10.3390/math9202529 - 09 Oct 2021

Cited by 4 | Viewed by 2248

Abstract

The present study comprises a comparison of the Mel Frequency Cepstral Coefficients (MFCC), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as feature extraction methods using ten different regression algorithms (AdaBoost, Bayesian Ridge, Decision Tree, Elastic Net, k-NN, Linear Regression, MLP, Random Forest, Ridge Regression and Support Vector Regression) to quantify the blood glucose concentration. A total of 122 participants—healthy and diagnosed with type 2 diabetes—were invited to be part of this study. The entire set of participants was divided into two partitions: a training subset of 72 participants, which was intended for model selection, and a validation subset comprising the remaining 50 participants, to test the selected model. A 3D-printed chamber for providing a light-controlled environment and a low-cost microcontroller unit were used to acquire optical measurements. The MFCC, PCA and ICA were calculated by an open-hardware computing platform. The glucose levels estimated by the system were compared to actual glucose concentrations measured by venipuncture in a laboratory test, using the mean absolute error, the mean absolute percentage error and the Clarke error grid for this purpose. The best results were obtained for MCCF with AdaBoost and Random Forest (MAE = 11.6 for both). Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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18 pages, 2499 KiB

Open AccessArticle

Automatic Feature Selection for Stenosis Detection in X-ray Coronary Angiograms

by Miguel-Angel Gil-Rios, Igor V. Guryev, Ivan Cruz-Aceves, Juan Gabriel Avina-Cervantes, Martha Alicia Hernandez-Gonzalez, Sergio Eduardo Solorio-Meza and Juan Manuel Lopez-Hernandez

Mathematics 2021, 9(19), 2471; https://0-doi-org.brum.beds.ac.uk/10.3390/math9192471 - 03 Oct 2021

Cited by 5 | Viewed by 1867

Abstract

The automatic detection of coronary stenosis is a very important task in computer aided diagnosis systems in the cardiology area. The main contribution of this paper is the identification of a suitable subset of 20 features that allows for the classification of stenosis cases in X-ray coronary images with a high performance overcoming different state-of-the-art classification techniques including deep learning strategies. The automatic feature selection stage was driven by the Univariate Marginal Distribution Algorithm and carried out by statistical comparison between five metaheuristics in order to explore the search space, which is

O (2^{49})

computational complexity. Moreover, the proposed method is compared with six state-of-the-art classification methods, probing its effectiveness in terms of the Accuracy and Jaccard Index evaluation metrics. All the experiments were performed using two X-ray image databases of coronary angiograms. The first database contains 500 instances and the second one 250 images. In the experimental results, the proposed method achieved an Accuracy rate of

0.89

and

0.88

and Jaccard Index of

0.80

and

0.79

, respectively. Finally, the average computational time of the proposed method to classify stenosis cases was ≈0.02 s, which made it highly suitable to be used in clinical practice. Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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14 pages, 2862 KiB

Open AccessArticle

Optic Disc Preprocessing for Reliable Glaucoma Detection in Small Datasets

by José E. Valdez-Rodríguez, Edgardo M. Felipe-Riverón and Hiram Calvo

Mathematics 2021, 9(18), 2237; https://0-doi-org.brum.beds.ac.uk/10.3390/math9182237 - 12 Sep 2021

Cited by 4 | Viewed by 6505

Abstract

Glaucoma detection is an important task, as this disease can affect the optic nerve, and this could lead to blindness. This can be prevented with early diagnosis, periodic controls, and treatment so that it can be stopped and prevent visual loss. Usually, the detection of glaucoma is carried out through various examinations such as tonometry, gonioscopy, pachymetry, etc. In this work, we carry out this detection by using images obtained through retinal cameras, in which we can observe the state of the optic nerve. This work addresses an accurate diagnostic methodology based on Convolutional Neural Networks (CNNs) to classify these optical images. Most works require a large number of images to train their CNN architectures, and most of them use the whole image to perform the classification. We will use a small dataset containing 366 examples to train the proposed CNN architecture and we will only focus on the analysis of the optic disc by extracting it from the full image, as this is the element that provides the most information about glaucoma. We experiment with different RGB channels and their combinations from the optic disc, and additionally, we extract depth information. We obtain accuracy values of 0.945, by using the GB and the full RGB combination, and 0.934 for the grayscale transformation. Depth information did not help, as it limited the best accuracy value to 0.934. Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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21 pages, 1046 KiB

Open AccessArticle

Classification of Diseases Using Machine Learning Algorithms: A Comparative Study

by Marco-Antonio Moreno-Ibarra, Yenny Villuendas-Rey, Miltiadis D. Lytras, Cornelio Yáñez-Márquez and Julio-César Salgado-Ramírez

Mathematics 2021, 9(15), 1817; https://0-doi-org.brum.beds.ac.uk/10.3390/math9151817 - 31 Jul 2021

Cited by 23 | Viewed by 11330

Abstract

Machine learning in the medical area has become a very important requirement. The healthcare professional needs useful tools to diagnose medical illnesses. Classifiers are important to provide tools that can be useful to the health professional for this purpose. However, questions arise: which classifier to use? What metrics are appropriate to measure the performance of the classifier? How to determine a good distribution of the data so that the classifier does not bias the medical patterns to be classified in a particular class? Then most important question: does a classifier perform well for a particular disease? This paper will present some answers to the questions mentioned above, making use of classification algorithms widely used in machine learning research with datasets relating to medical illnesses under the supervised learning scheme. In addition to state-of-the-art algorithms in pattern classification, we introduce a novelty: the use of meta-learning to determine, a priori, which classifier would be the ideal for a specific dataset. The results obtained show numerically and statistically that there are reliable classifiers to suggest medical diagnoses. In addition, we provide some insights about the expected performance of classifiers for such a task. Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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25 pages, 1333 KiB

Open AccessArticle

Supervised Classification of Diseases Based on an Improved Associative Algorithm

by Raúl Jiménez-Cruz, José-Luis Velázquez-Rodríguez, Itzamá López-Yáñez, Yenny Villuendas-Rey and Cornelio Yáñez-Márquez

Mathematics 2021, 9(13), 1458; https://0-doi-org.brum.beds.ac.uk/10.3390/math9131458 - 22 Jun 2021

Viewed by 2122

Abstract

The linear associator is a classic associative memory model. However, due to its low performance, it is pertinent to note that very few linear associator applications have been published. The reason for this is that this model requires the vectors representing the patterns to be orthonormal, which is a big restriction. Some researchers have tried to create orthogonal projections to the vectors to feed the linear associator. However, this solution has serious drawbacks. This paper presents a proposal that effectively improves the performance of the linear associator when acting as a pattern classifier. For this, the proposal involves transforming the dataset using a powerful mathematical tool: the singular value decomposition. To perform the experiments, we selected fourteen medical datasets of two classes. All datasets exhibit balance, so it is possible to use accuracy as a performance measure. The effectiveness of our proposal was compared against nine supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, support vector machines, and neural networks), including three classifier ensembles. The Friedman and Holm tests show that our proposal had a significantly better performance than four of the nine classifiers. Furthermore, there are no significant differences against the other five, although three of them are ensembles. Full article

(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms)

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