PREDICTING THE STAGES OF BIPOLAR DISORDER USING MACHINE LEARNING ALGORITHMS

Bipolar disease, a complicated disease in mind and it affected many hundreds of thousands of human beings across the world. This mind disease is recognized via means of the prevalence of the oscillations of the patient’s changing mood. The mood swings among states i.e. melancholy and mania in mind. This is a end result of various mental and bodily capabilities. A set of psycholinguistic capabilities like behavioral changes, temper swings and intellectual contamination are located to offer remarks on fitness and wellness. The look at is a goal degree of figuring out the strain degree of human mind that might enhance the dangerous results related to it considerably. In the paper, we look at prediction of signs of bipolar disease by using the Machine Learning Techniques. Therefore, we extracted facts from articles and study papers and analyzed via way of means of the use of statistical evaluation elements and Machine learning (ML) techniques. Data is visualized to extract and talk significant facts from complicated datasets on predicting and optimizing numerous day to day analyses. In this paper we consist of the numerous studies papers having Machine Learning algorithms and classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed. The reason of the paper is particularly to discover the challenges, adequacy and boundaries in detecting the intellectual fitness situations by using Machine Learning Techniques.