Classification Algorithms based Mental Health Prediction using Data Mining

The emotional, psychological and social welfare of a person is revealed by their mental health. It influences how an individual will think, feel or handle a situation. Positive mental health helps an individual to work productively and achieve their full potential. At each point in life, mental health is vital, from childhood to adulthood. Numerous factors contribute to mental health issues which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, workplace issues and personality disorders. The onset of mental illness should be determined without flaws for maintaining an appropriate life balance. We have collected data from online available datasets. The data has been label encoded for better prediction. The data is being subject to various machine learning techniques to obtain labels. These classified labels will then be used to build a model to predict the mental health of an individual. The accuracy of the algorithm will be analysed before it is used to build the model. We planned to implement classification algorithms such as Decision Tree, Random Forest and Naïve Bayes. Our target population is in the working class i.e people above the age of 18. Once the model is built, it will be integrated to a website so that it can predict the outcome as per the details provided by the user.