An intelligent disease prediction and monitoring system using feature selection, multi-neural network and fuzzy rules

This work proposes a new intelligent disease prediction system for predicting the disease and also knowing the current status of the dead diseases such as diabetic, heart and cancer diseases. More number of people are affecting and losing their life early due to these diseases so that these are also called as dead diseases. The proposed disease prediction and monitoring system consists of two phases such as feature selection and classification phases. In the feature selection phase, a newly proposed feature selection algorithm called conditional random field and mutual information-based feature selection algorithm is used for identifying the most contributed features that are used to enhance the prediction accuracy. In the classification phase, a newly proposed fuzzy-aware multilayer backpropagation neural network is applied for predicting and monitoring the diabetic disease and heart disease effectively. Here, newly generated fuzzy rules are also incorporated for making effective decision on patient records. The proposed prediction and monitoring system is used to predict and monitor the heart, diabetic and cancer diseases. The experiments have been conducted for evaluating the performance of the proposed disease prediction and monitoring system by using UCI Machine Learning Repository datasets and also proved that as better than the existing disease prediction systems in terms of precision, recall, F-measure and prediction accuracy.