Classification Algorithms based Mental Health Prediction using Data Mining


Mental health reveals a person's emotional, psychological, and social well-being. It has an impact on how a person thinks, feels, and reacts to a situation. A person's mental health can help them operate more productively and reach their full potential. Mental health is important at every stage of life, from childhood to adulthood. Stress, social anxiety, depression, obsessive compulsive disorder, substance addiction, workplace challenges, and personality disorders are all variables that contribute to mental health concerns that lead to mental disease. In order to preserve a healthy life balance, the onset of mental disease should be accurately determined. We gathered information from publicly available datasets on the internet. For better prediction, the data has been label encoded. To obtain labels, the data is subjected to a variety of machine learning approaches. These categorised labels will then be utilized to create a model that can predict an individual's mental health. Before the algorithm is used to generate the model, its correctness will be evaluated. We planned to use Decision Tree, Random Forest, and Nave Bayes as classification methods. Our target demographic is the working class, defined as persons over the age of 18. The model will then be implemented into a website, allowing it to anticipate the outcome based on the information provided by the user.

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