Predictive Modelling in Mental Health: A Data Science Approach

In national and regional level, understanding of factors associated with public health issues like mental health is paramount important to improve the awareness. This study aims to use the data mining techniques such as association rule mining to improve the degree of understanding the mental health among various geographical areas by identifying various vital behavioural factors associated with mental health issues. The study will produce interesting relationships among the behavioural factors in form of Association rules. The outcomes of this research will be beneficial to organisations that work in public health sector to improve mental health among the citizens. Also, this proposed new data science approach will be beneficial to improve the degree of understanding by identifying factors associated with mental health within the different geographical areas such as city or state level. The study found that states in US which have low excessive drinking percentage and high obesity and high smoking percentage has the highest frequent of mental distress. Also, these rules have shown high confidence threshold among females rather than males. Mental health related authorities who works in local and national governments may be used these findings to improve awareness of mental health.