APPLICATIONS OF MACHINE LEARNING TECHNIQUES FOR DISEASE DIAGNOSIS: A REVIEW

Abstract The proliferation of machine learning techniques in different capacities for real life problems has changed the way in which problems are perceived and solved. We study the present status of ML applications in medical care and explore their potential. Machine Learning can be employed for different varieties of healthcare data (structured and unstructured). Some well-known ML techniques which are used for diagnosing diseases like cancer, diabetes mellitus, hepatitis, and cardiovascular diseases include Neural Network (NN), K-Nearest Neighbor (K-NN), Decision Tree (DT) and classical Support Vector Machine (SVM). Within this exploration, the utilization of machine learning for detection and diagnosis of diseases is studied. The key focus is to discover machine learning techniques (MLT), which are extensively utilized to anticipate, forecast and treat vital regular illnesses, for example, malignancies (cancers), diabetes, hepatitis, and cardiovascular diseases.