A Linear Model Based on Principal Component Analysis for Disease Prediction

Various classification methods are applied to predict different diseases, such as diabetes, tuberculosis, and so on, in medical field. Diagnosis of diabetes can be analyzed by checking the level of blood sugar of patient with the normal known levels, blood pressure, BMI, skin thickness, and so on. Several classification methods have been implemented on diabetes. In this paper, the main aim is to build a statistical model for diabetes data to get better classification accuracy. Extracted features of diabetes data are projected to a new space using principal component analysis, then, it is modeled by applying linear regression method on these newly formed attributes. The accuracy obtained by this method is 82.1% for predicting diabetes which has reformed over other existing classification methods.