Automatic Diabetes and Liver Disease Diagnosis and Prediction Through SVM and KNN Algorithms

Abstract

Advances in data mining and machine learning methods for classification and regression open the door of identifying complex patterns from domain sensitive data. In biomedical applications, massive amounts of clinical data are generated and collected to predict diseases. Diagnosis of diseases, such as diabetes and liver diseases, needs more tests these days and that increases the size of patient medical data. Manual exploration of this patient test data is challenging and difficult. Robust prediction of a patient’s liver disease from the massive data set is an important research agenda in health science. The challenge of applying a machine learning method is to select the best algorithm within the disease prediction framework. The goal of this research is to select a robust machine learning algorithm that can equally be applicable on diabetes prediction as well as in liver diseases prediction. This study analyzes two machine learning approaches, support vector machine (SVM) and K-nearest neighbors (KNN) algorithms over two different datasets, diabetes and liver diseases datasets. It was observed that a tuned radial SVM method performed with the highest accuracy in detection of diabetes and liver disease detection with an accuracy of 0.989 for diabetes detection and 0.910 for liver disease detection.