Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naive Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

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The Role of Machine Learning Algorithms for Diagnosing Diseases

This study comprehensively compared two hydrothermally synthesized S-scheme heterojunctions, Bi2WO6/g-C3N4 and Bi2WO6/TiO2. The photocatalytic removal of cefixime (CFX) was used to screen the different mass ratios of the components for each heterojunction. Photocatalytic adsorption/degradation and operational effects such as initial pH, the ratio of CFX concentration to the photocatalyst load, light intensity, UV irradiation, and the presence of anions were compared and evaluated. The adsorption isotherms and kinetics of the photocatalytic adsorption and degradation were studied. Furthermore, the band structure was investigated by valence band X-ray photoelectron spectroscopy (VB-XPS), Mott-Schottky plot, and UV–vis DRS. The mechanism of the photocatalytic reaction under visible and UV–vis irradiation was comprehensively investigated by scavenger tests and electron spin resonance (ESR). The photocurrent response, EIS, and linear sweep voltammetry (LSV) results confirmed the photocatalytic enhancement of the heterojunctions. The leaching of metal ions, reusability, and performance of the heterojunctions were investigated for 6 cycles. The photocatalytic degradation pathway of CFX and the toxicity of the by-products were investigated by LC-MS and Toxicity Estimation Software Tool (T.E.S.T). After 135 min of photocatalytic reactions, the TOC removal efficiency of CFX was 94 % and 91 % for Bi2WO6/g-C3N4 and Bi2WO6/TiO2. CFX and the by-products were entirely mineralized after 180 min of the reactions. It was found that the binary heterojunctions and the photocatalytic reactions are green and environmentally friendly. The optimized artificial neural network with 18 neurons simulated the experiments. The trained feed-forward network was able to successfully simulate different operating conditions and different mass ratios of the heterojunctions. 

A comprehensive comparison of green Bi2WO6/g-C3N4 and Bi2WO6/TiO2 S-scheme heterojunctions for photocatalytic adsorption/degradation of Cefixime: Artificial neural network, degradation pathway, and toxicity estimation

Data mining (DM) is an instrument of pattern detection and retrieval of knowledge from a large quantity of data. Many robust early detection services and other health-related technologies have developed from clinical and diagnostic evidence in both the DM and healthcare sectors. Artificial Intelligence (AI) is commonly used in the research and health care sectors. Classification or predictive analytics is a key part of AI in machine learning (ML). Present analyses of new predictive models founded on ML methods demonstrate promise in the area of scientific research. Healthcare professionals need accurate predictions of the outcomes of various illnesses that patients suffer from. In addition, timing is another significant aspect that affects clinical choices for precise predictions. In this regard, the authors have reviewed numerous publications in this area in terms of method, algorithms, and performance. This review paper summarized the documentation examined in accordance with approaches, styles, activities, and processes. The analyses and assessment techniques of the selected papers are discussed and an appraisal of the findings is presented to conclude the article. Present statistical models of healthcare remedies have been scientifically reviewed in this article. The uncertainty between statistical methods and ML has now been clarified. The study of related research reveals that the prediction of existing forecasting models differs even if the same dataset is used. Predictive models are also essential, and new approaches need to be improved.

Smart healthcare disease diagnosis and patient management: Innovation, improvement and skill development

In classification and prediction of different types of medical disorders the neuro-fuzzy systems (NFS) are playing vital and significant role. To avoid false diagnosis the NFS assists medical practitioners to a greater extent in automating the domain dealing with medical disorders. With the passage of time the NFS approach has become apparent to enhance accuracy in dealing with a wide range of complicated research problems in the field of medical diagnosis. In this paper the author presents the literature review of the research done in implementing NFS in the field of medical diagnosis for current decade. Total of 100 publications in chronological advancement and up-gradation in models are considered for the time period of 10 years. A detailed study of each disease is carried out to discuss how NFS methodologies have been applied for classification and prediction in the diagnosis of different types of medical disorders. Ten (10) most severe medical disorders i.e. cancer, cardiovascular, depression and anxiety, diabetes, communicable, kidney, liver, neuro-degenerative, respiratory and thyroid has been undertaken for the study. Based on the study carried out it has been observed that NFS found to be effective as compared to the application of other AI techniques in medical diagnosis. Study reveals that effectiveness of NFS increases significantly when integrated with other AI approaches. This review adds into the knowledge of different researchers working in the field of medical diagnosis and will also give the comprehensive view of the effectiveness of the NFS techniques being used in medical diagnosis. The paper also incorporates a few research publications that were submitted in 2019 to incorporate the latest advances in medical science implementation of NFS.

Usage and implementation of neuro-fuzzy systems for classification and prediction in the diagnosis of different types of medical disorders: a decade review

Estimation of the energy production of a parabolic trough solar thermal power plant using analytical and artificial neural networks models

Chronic Kidney Disease (CKD) is a major public health concern with rising prevalence. In Saudi Arabia, approximately 2 Billion Riyals are solely allocated for renal replacement therapy which is required for patients with advanced stages of CKD. Therefore, this study aims to decrease the number of patients and the expenses needed for treatment by preemptively diagnosing chronic kidney disease accurately using data mining and machine learning techniques. Data have been collected from a region that has never been explored before in literature. This study uses Saudi data retrieved from King Fahd University Hospital(KFUH) in Khobar to carry out the experiment. Experimental Results show that ANN, SVM, Naive Bayes achieved a testing accuracy of 98.0% while k-NN has achieved an accuracy of 93.9%.

Preemptive Diagnosis of Chronic Kidney Disease Using Machine Learning Techniques

Diabetic retinopathy (DR) is one of the major leading diseases of vision loss in middle-aged men and women. It refers to the changes that happen to the retina of individuals suffering from diabetes for a long time. In this chapter, we present the results from using the Adaptive Neuro-Fuzzy Inference System (ANFIS) to classify the retina’s level of damage caused by DR to four classes (low, moderate, high, and not damaged). Initially, a dataset was built out of the publicly available DIARETDB0 dataset, by giving an estimated value for DR risk factors and symptoms for each retinal image in the DIARETDB0 dataset. The risk factors considered in this experiment include: diabetes duration, age, sugar level, blood pressure, cholesterol level, and hemoglobin level. The DR symptoms are microaneurysms, hemorrhages, and exudates. Two techniques were used for performing the feature selection: the correlation coefficient and principal component analysis (PCA), along with different types of membership functions, and their results were compared. The experimental results show that using half of the features with the highest correlation gives better performance with 84.09% accuracy, root-mean-square-error (RMSE) of 0.476 and a Kappa coefficient of 0.79. While using PCA, the accuracy was 65.9%, RMSE was 0.738 and Kappa coefficient of 0.544.

A Neuro-Fuzzy Inference Model for Diabetic Retinopathy Classification

Diabetes Mellitus (DM) is one of the most prevalent chronic diseases in the world with around 150 million patients. Patients with chronic diseases are highly susceptible to deterioration in their physical and mental health; consequently, hindering their independence, restricting their daily activities imposing a large financial burden on them and the government. If not discovered early, chronic diseases may lead to serious health complications or in extreme cases, death. Diagnostic solutions have been explored using intelligent methods, however, different ethnic groups have variant factors leading to the development of a disease. Therefore, the proposed system aims to preemptively diagnose DM in a region never explored before. Data are retrieved from King Fahd University Hospital (KFUH) in Khobar, Saudi Arabia. Data undergoes preprocessing to identify relevant features and prepare for identification/classification process. Experimental results show that ANN outperformed SVM, Naive Bayes, and K-Nearest Neighbor with the testing accuracy of 77.5%.

Preemptive Diagnosis of Diabetes Mellitus Using Machine Learning

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Preemptive Diagnosis of Chronic Kidney Disease Using Machine Learning Techniques

A Neuro-Fuzzy Inference Model for Diabetic Retinopathy Classification

Preemptive Diagnosis of Diabetes Mellitus Using Machine Learning