Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Abstract

Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.

A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases

“…In Ref. [17], authors have presented a study to diagnose heart diseases, AIDS, brain cancer, diabetes, dengue, and hepatitis C. e machine-learning algorithms, Naive Bayes, J48, K-Nearest Neighbors, and C4.5 algorithms, have been employed to map the patients onto different classes of the above-stated diseases. e comparisons of the results among harnessed classifiers have revealed that C4.5 outperformed with 83.6% prediction accuracy, followed by J48 and Naive Bayes classifiers achieving 81.1% and 75.97% prediction accuracies, respectively.…”

Technology Management for Accelerated Recovery during COVID-19

“…Additionally, Caballé et al (2020) believe that technology is currently well suited for analyzing medical data and presents a wide range of possible applications. Unlike early A.I.…”

“…According to Caballé et al (2020) in Artificial Intelligence, Machine Learning stands out as a method for providing tools for intelligent data analysis. There are specific algorithms that are used to develop models with predictive capabilities, and the selection of such algorithms depends on the research objective.…”

“…The default model for given research is based on Decision Trees (DT) that are predictive representations of datasets. Decision trees are a hierarchical way of partitioning the space, where the goal is to develop a model that predicts the value of a target variable based on several input variables (Caballé et al, 2020).…”

Future Forecasting of COVID-19: A Supervised Learning Approach

“…Based on the results from the testing phase, error rate and misclassification are calculated [1]. One of the most popular purposes for which ML has been applied is forecasting [2][3][4][5]. Several ML algorithms have been used to predict future events in applications such as weather forecasting and disease diagnosis.…”