Machine Learning, Deep Learning and Image Processing for Healthcare: A Crux for Detection and Prediction of Disease

Abstract

AbstractMachine learning has rapidly gained traction in a variety of fields, including science, healthcare, engineering, and biotechnology, in recent years owing to its effective functioning mechanism. Health care has always been a key priority for any government as the industry has made significant progress by using machine learning, artificial intelligence, and deep learning for disease prediction and diagnosis. The central aspect of this paper is to evaluate different machine learning algorithms and classification techniques in order to detect and predict different chronic diseases. The paper discusses supervised classification strategies for detecting diseases such as cancer, psychological disorders, and cardiac disorders, as well as various bioinformatics and biomedical research challenges. The comparison between classification techniques like support vector machines, logistic regression, decision trees, random forest, and Naïve Bayes classifiers has been observed in numerous diseases. The algorithms that are specifically applied in the medical applications and in healthcare sector enabled the clinical experts and physicians to watchdog, diagnose, and monitor the disease effectively and perform appropriate measures in the shortest possible duration. Decision support systems benefit the physicians for effective and timely decision-making capabilities in case of chronic diseases. The paper reviews the implementation of machine learning and deep learning which has undoubtedly contributed toward health informatics, healthcare systems including bioinformatics. After discussing the techniques and comparison of numerous classification algorithms in several diseases, the ones which have efficiently produced an appropriate result in an early detection of diseases have been highlighted. In order to emphasize the issues that must be considered while implementing the methodologies and classification algorithms for an early illness detection system, several future directions are described.KeywordsBioinformaticsHealthcareChronic disease detectionMachine learningDeep learningImage processingCancer detectionPsychiatric disorderEarly disease detectionDisease prediction