An adaptive soft set based diagnostic risk prediction system

Abstract

Recently, risk based prediction models in medical diagnostic systems gain wider significance in deciding most appropriate diagnostic treatments and for clinical usage. Prostate cancer is a disease which is difficult to diagnose and there are number of failure cases reported. Therefore, an effective and aggressive selection of multiple factors influence on the disease is required. In this paper, an adaptive soft set based diagnostic risk prediction system is presented with the implementation on prostate cancer. The system receives input parameters related to the disease and gives out the risk percentage of the patient. Soft sets are generated with the input parameters by fuzzification followed by rule generation. The risk percentage of the rules are individually calculated for Precision, Recall and F-Measure, that conclude on the best risk percentage based on the maximum area under the curve (AUC) in each case. This ensures to select the most influential risk parameters in treating the disease. Specificity and sensitivity of the test system yield 75.00% and 45.45% respectively.