Rule induction using enhanced RIPPER algorithm for clinical decision support system

Due to availability of large amount of data with the emergence of computers and internet, data mining is getting popular in every field of life like business, health, disasters etc for predictive analysis. As more and more data becomes available, it becomes difficult to get useful information from that. In that case, that tremendous data is quite useless. For that purpose data mining comes as a savior and helps us to extract useful information out of the data. This information can be used further for decision making. This paper presents a model that helps in diagnosis of diseases by analyzing the patients' data. The patients' attributes are analyzed and association rules are extracted from these attributes. Association rule based Classification is used for disease diagnosis and thus helpful in clinical decision making. A patient is classified as healthy or sick based on his attributes using classification. Disease Mining Model is proposed (DMM) based on association rules mining (ARM). This model is globally optimized by using Weighted Association Rules Mining (WARM) as Optimized Disease Mining Model (ODMM) which provides improved accuracy of disease prediction for every disease dataset. Both DMM and ODMM are tested on nine datasets of different diseases. Results of disease diagnosis are verified against real diagnosis. WARM improves the accuracy of diagnosis and thus outperforms ARM. Thus in this work, Classification using Ripper algorithm is much improved using weight optimization.