Implementing machine learning techniques to predict bipolar disorder

Bipolar disorder (BD) is a mental and psychiatric disorder which is characterised by alternate mode swings between mania and depression is very common these days. The classification, modelling and characterisation and diagnosis of these mental disorders are important in medical research. An unexpected and unexplored area in BD is to judge the non-verbal behaviour of person accurately. Therefore, this paper address the challenges of detecting BD state by machine learning (ML) techniques to test the non-verbal behaviours activities like various facial expressions, voice recordings and body gestures of mentally ill and controlled persons in a whole spectrum. ML techniques can potentially provide new horizons in diagnosing and treating in mental healthcare. Further, this paper aims to present commonly used algorithms such as decision trees (DT), support vector mechanism (SVM), logistics regression (LR), K-nearest neighbours (KNN), etc. and describe their properties and performances which could act as a guide to select appropriate models. The study shows that people with controlled state behaves significantly different as compared to BD patients in their interpersonal accuracy (IPA). This develops a new training program to improve better understanding and psychosocial functionality in their rehabilitation.