Performance Analysis of Recursive Rule Extraction Algorithms for Disease Prediction

Modern busy lifestyles are acting as a catalyst to enhance the growth of various health-related issues among people. As a consequence, a massive amount of medical data are getting accumulated every day. So, it is becoming a challenging task for the medical community to handle those data. In such a situation, if a system exists that can effectively analyze those data and can retrieve the primary causes of a disease, then the disease can be prevented on time by taking the correct precautionary measures beforehand. Recently, machine learning algorithms have been receiving a lot of appreciation in building such an expert system, and the neural network is one of them which has attracted a lot of researchers due to its high performance. But the main obstacle which hinders the application of neural networks in the medical domain is its black-box nature, i.e. its incapability in making a transparent decision. So, as a solution to this problem, the rule extraction process is becoming very popular as it can extract comprehensible rules from neural networks with high accuracy. Many rule extraction algorithms exist in the literature, but this paper mainly assesses the performances of the algorithms that generate rules recursively from neural networks. Recursive algorithms recursively subdivide the subspace of a rule until the accuracy increases. So, they can provide comprehensible decisions along with high accuracy. Four medical datasets are collected from the UCI repository for assessing the performances of the algorithms in diagnosing a disease. Results prove the effectiveness of the recursive rule extraction algorithms in medical diagnosis.