Classification of acute disease detection symptoms is a big issue in an automatic healthcare system. The automatic healthcare system provides telemedicine for rural and urban citizens worldwide. The classification algorithm estimated the disease behaviors and suggested the medicine for the patients. Machine learning offers various algorithms for mining. The bucket of machine learning techniques is association rule mining, clustering, classification and regression. The classification of medical disease data is very intensive. Most authors applied NB algorithms for better detection of disease. This paper presents the experimental analysis of medical disease data based on various machine learning algorithms. The clustering and classification technique play a significant role in health care databases. The process of clustering defines the process of similar patterns based on iteration. Instead of data, the classification technique guided the grouping technique based on specific guidance. The central complex problem in the classification process is a selection of features of disease data. Most disease data features are similar, and the applied classifier detects the false value of classification. The importance of feature selection in medical disease data classification is paramount. The process of analysis is based on experimental. The experiment uses MATLAB software and reputed disease datasets from the UCI machine learning site.