Review on Machine Learning Techniques for Medical Data Classification and Disease Diagnosis

Purpose Machine learning (ML) has become a major trend in the industry because it is a new and extremely advanced technical application. Design ML is utilized in various areas including medicine, finance, and defense. Nowadays, a vast amount of data is readily accessible. As a result, analyzing this data to obtain any valuable information is critical. Machine learning algorithms have been applied recently to find trends in medical data and have proven to be extremely accurate predictors. Findings ML techniques make correct predictions based on the past experiences. Therefore, it is a great approach which aids in the disease prediction and diagnosis. With the advancement of technology, the medical sector has benefited greatly from the convergence of ML and wearable technology in order to provide seamless solutions that are extremely accurate, efficient, and durable. In this review, medical data classification is analyzed with decision support system, medical image analysis, regularization techniques, and CBFL. By analyzing various approaches, better approaches in terms of accuracy were obtained for feature extraction, classification, and disease diagnosis process. This benefits patients by allowing for early detection and lower medical expenditures, as well as providing the medical community with efficient, scalable, accurate, and trustworthy prediction tools. Originality In this paper, we look at some of the machine learning algorithms that have been used to establish successful decision support for healthcare applications and disease diagnosis. This review also discussed the classification of medical data, feature selection of medical data, decision support system, medical image analysis, etc. Lay Summary In recent years, machine learning (ML) has the major impact due to its progressive technical applications. The techniques of machine learning provide accurate prediction based on past experiences. There are numerous ML approaches that have been developed and applied for disease classification and diagnosis. Analysing ML-based techniques provides valuable information to resolve critical research issues. In this review, various ML approaches are analyzed with respect to medical data classification and disease diagnosis. In addition to that, decision support system, medical image analysis, regularization techniques, and CBFL are analyzed. It directs to select efficient ML technique for accurate classification of medical data or disease diagnosis. In future work, public image dataset with various imaging modalities will be considered for the classification task and the given information will be analyzed in various aspects. The computer-aided diagnosis of different image modalities with hybrid ML architecture from medical data will be investigated for early disease classification and diagnosis.