Predictive Research For Mental Health Disease
Shaifali Chauhan1, Ankur Garg2

1Shaifali Chauhan, Computer Science and Engineering, M.I.E.T, Meerut, Uttar Pradesh India.

2Ankur Garg, Computer Science and Engineering, M.I.E.T, Meerut, Uttar Pradesh India.

Manuscript received on 09 August 2019 | Revised Manuscript received on 16 August 2019 | Manuscript Published on 31 August 2019 | PP: 196-199 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I10380789S219/19©BEIESP DOI: 10.35940/ijitee.I1038.0789S219

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Abstract: Many people are suffering from some kind of mental illness and this number is increasing day by day. Despite major revolutions in medical science exact identification of factor that leading to mental illness is still unknown to the world. Due to its ambiguous nature, mental state of person is a major focus on research these days. With the emergence of smart phones, PCs, internet of things. The amount of data human kind produce everyday is huge and only accelerating. These data are stored in a semi structured way and used to get meaningful relationships and trends in data. Data mining techniques can be efficiently used on this data to find hidden patterns between different attributes of data. This paper describes the prototype to use data mining technique namely Random forests classification to determine person’s mental state based on attributes such as age, gender, life style, education, Occupation, personal income, vision, sleep, mobility, hypertension, diabetes. The system will predict whether a patient is suffering from mental illness or not.

Keywords: Data mining, Random Forest, Decision tree, Knowledge Discovery
Scope of the Article: Agent-Based Learning and Knowledge Discovery