Estimating The Accuracy of Classifiers in Analyzing Multiple Diseases

Medical data are regarded as been sensitive not only in terms of the need to keep it private but also and majorly in terms of the need to get it right and accurate. Patients’ medical data are diagnose and analyze with optimal accuracy to avoid error of prescription. Multiple diseases are one that can easily get complicated where the analysis of symptoms are not right. Machine learning is a known field of inquiry found very suitable in the medical area for analysis of medical diagnosis. The need for the right classification algorithm to deploy for a particular medical experimentation/prediction becomes very germane especially in the case of multiple diseases. No doubt, many researches have been done in this regard but not specifically tailored towards multiple diseases. The study which utilizes medical data from third party, www.kaggle.com, applied selected common three classification algorithms on the dataset. The result of the experimentation carried out using WEKA Explorer, shows Artificial Neural Network (ANN) outperforms Decision Tree and Naïve Bayes in terms of level of accuracy.