RETRACTED ARTICLE: Multi feature drug compound analysis model for efficient success rate prediction using fuzzy rules

Abstract

The human society has been identified as more prone for diseases. Various drugs with different compounds are available to treat the diseases. However, the medical practitioner cannot be sure about the application of the drug and what would be exact drug should be feed to the patient. To hook this, a multi feature drug compound analysis model is presented in this paper. The method keeps track of medical records related to various patients and the details of drugs being provided to them. Using these treatment data set, the method applies machine learning techniques to generate and predict the success rate of different drugs. To perform this, the method first split the records based on the disease and for each of them the list of medicines and compounds given has been identified. Based on these data, a set of patterns are generated according to various compounds of drug provided. Further, the method estimates the success influence measure (SIM) for different drug components. Estimated success influence measure is used to generate the fuzzy rules. Based on the rule generated, the method performs success rate prediction for various drug compounds. The method produces noticeable growth in the success rate prediction.