Smart Mental Stress Predictive System for Healthcare Using Data Mining Techniques
A huge amount of medical data is available in healthcare that collects from patients. Often receives two to three mental health symptoms like, heart beating and blood sugar, a patient is to be considered that suffering from mental disaster which is not confirm enough. This research developed and provides a model of Mental Stress Predictive System (MSPS) by using advance data mining technique (DMT), namely Decision tree and Naïve Bayes that would help organization to predict the patient’s mental health. For this, various other factors have taken such as, chest pain, head ache, heart palpitation etc, and according to these factors, MSPS predicts the patient status. The MSPS is implemented under the .Net framework. Thus, results show that both DMT has distinct strength and works better than the traditional decision system. Lift chart and classification matrix method has used for the sake of calculating correct prediction.