A Comparative Analysis of Data Mining Techniques for Mental Health Classification Problem
Keywords:Mental health classification, data mining, precision, accuracy
Mental health is a very dangerous illness that can bring death to the patient as well as people around them if it is not treated properly at early stage of the illness. Mental health is dangerous because it involves the person psychological and it can make the patients either aggressive or passive. Early detection is crucial in helping experts and practicians to classify them from as normal person. This paper will discuss the use of data mining techniques for classifying the tech worker with mental health problems. We select different methods of data mining techniques to find the most accurate results related to mental health. The technique that will be using is decision tree, Naïve Bayes, and neural network. Based on the three techniques, we will find which of the technique has better accuracy, better precision, and less time taken.