Early Forecasting and Prevention of Cardio Vascular Disease Based on Human Life Style Factors

In the rapid growing world living fashion of human beings are distorted a lot and will influence the healthiness situation of human beings. The World Health Organization (WHO) statistics provides information that the cardio vascular diseases have an immense deal of interest inmedical research owing to its impact on person healthiness. This research study is intended torecognize and analyze the human life style factors in forecasting the cardio vascular disease with data mining techniques. The data collected from the range of citizens living in Bangaloreregion and the data obtainable inside online data repository are the input to this research analysis. Classification techniques K-means clustering algorithm and the decision tree approaches are used in this study and the precision, recall, accuracy and the F-measures are the measures estimated to prove the results with the different error measures RMSE, MAE and Kappa statistic values. This early forecasting process is proved with an accuracy of 94.56 percentages in K-means and 93.37 percentages with decision tree techniques.