Evaluation of Predictive Machine Learning Techniques as Expert Systems in Medical Diagnosis

Background/Objectives: Medical science industry has immense measure of information; however a large portion of this information is not mined .Machine Learning takes analytics to the extreme by exploring hidden information in data. Diagnosis of a disease is major objective of medical decision support system which will help the physicians to take effective decision Methods/Statistical analysis: In this research paper Machine Learning techniques, K-Nearest Neighbors (KNN), Decision Tree , Artificial neural networks (ANNs), Radial Basis Function (RBF) neural networks and Support Vector Machine (SVM) are analyzed . Findings: Performance of these techniques is compared through various performance measures such as sensitivity, specificity, accuracy, F measure, Kappa statistics, True Positive Rate, False Positive Rate and ROC on Breast Cancer Wisconsin, Liver Disorder, Hepatitis and cardiovascular Cleveland Heart disease datasets. Research work consists of 10 V-fold cross validation method to measure the fair estimate of these prediction techniques . Application/Improvements: The evaluation of these techniques on diverse medical datasets gave an insight into predictive ability of Machine Learning in medical diagnosis and there is a wide space of improvement.