Emotional-physic analysis using multi-feature hybrid classification

Abstract

On-time recovery and treatment of disease is always desirable. The use of Machine learning in health-care has grown very fast to diagnosis the different kinds of diseases in the past few years. In such a diagnosis, past and real-time data are playing very crucial role in using data mining techniques. Still, we are lacking in diagnosing the emotional mental disturbance accurately in the early stages. Thus,the initial diagnosis of depression expressively stances a great problem for both,researchers and clinical professionals. We have addressed the said problem in our proposed work using Pipeline Machine Learning technique where people based on emotional stages have been effectively classified into different groups in e-healthcare. To implement Hybrid classification, a well known machine learning multi-feature hybrid classifier is used by having the emotional stimulation in form of negative or positive people. In order to improve classification, an Ensemble Learning Algorithm is used which helps in choosing the more suitable features from the available genres-emotion data on online media. Additionally, Hold out validation method has been to split the dataset for training and testing of the predictive model. Further, performance evaluation measures have been applied to check the proposed system evaluation. This study is done on Genres-Tags MovieLens dataset. The experimental results show that applied ensemble method provides optimal classification performance by choosing the best subset of features. The said results proved the excellency of the proposed system which comes from the choosing most related features selected by the Integrated Learning algorithm. Additionally, suggested approach is used to accurately and effectively diagnose the depression in its early stage. It will help in recovery and treatment of depressed people. We conclude that use of the suggested method is highly suitable in all aspects of e-healthcare for depress stimulation.