Machine Learning Based Psychological Disease Support Model Assisting Psychoanalysts and Individuals in Clinical Decision Ministration

Abstract

Mental Pressure is a significant contributor to an individual's health and is directly linked with various diseases including depression and mental disorder. It is therefore extremely important to monitor the patient's misbehavior. Therapeutic-psychology based on machine learning algorithms clearly concentrates on studying multifaceted datasets to deduce statistical features in order to create generalized projections about patients. A novel paradigm to develop Psychological Disease Support Model (PDSM) will be discussed through this research paper. This model will support diagnosis of various psychological diseases like schizophrenia, bipolar disorder and obsessive compulsive disorder. It will take some health parameters as input from end user, process that data based on machine learning approach and provides desired outcome in user friendly way. Existing methods of wellness surveillance continue to confront many difficulties owing to inadequate information from healthcare records. The proposed approach seeks to develop a scheme of autonomous choice assistance for an initial treatment of important psychological occurrences. Machine based learning is widely described as a methodology that automatically knows how to solve the issue optimally, instead of being programmed by natural beings in order to provide a set answer. The suggested model comprises of machine-learning based diagnostic system which pre-processes the data available and applies classification procedures on it. The main objective of proposed model is to create interventions to enhance the health scores of patients that will be useful for psychiatrists and dedicated hospitals. Scientifically evaluated results obtained from the proposed model will be compared with other models of similar kind. The paper will also discuss various barriers while designing a psychological disease diagnosis decision support system.