UTILIZING MACHINE LEARNING ALGORITHMS TO PREDICT THE SEVERITY OF MAJOR DEPRESSIVE DISORDER

Abstract

Health care area is rising immensely from last few decades. It creates large amount of records about patients, clinical sources, infection analysis, electronic patient records, curative trials etc. Huge size records are a key source which needs preprocessing and evaluation for information mining that assists backing for cost-savings and verdict constructing. When medical organizations apply data mining on their prevailing data, they can realize new, beneficial and possibly life-saving information. Heterogeneity of major depressive disorder (MDD) disease progression muddles medical management. It can pretense dares for clinicians concerning correct analysis and actual well-timed cure. These challenges have provoked the increase of many machine learning techniques to aid advance the controlling of this disease. Machine learning (ML) is an application of artificial intelligence (AI) that offers structures the capability to mechanically acquire and improve from experience minus being explicitly programmed. Far-reaching data mining practices utilizing machine learning procedures have potential in the study of big epidemiological datasets. The following proposed research illustrates the idea of machine learning algorithms to analyze the depression epidemiological datasets in terms of its severity. Here, mean value substitution is performed on the data to preprocess it and then multiclass classifier machine learning algorithms are practiced to know the severity of the depression among the people at an early stage so that proper and correct timely treatment can be provided which will result in cost saving and faster improvement of health conditions of patients.