Fuzzy-Ontology based knowledge driven disease risk level prediction with optimization assisted ensemble classifier

Modern medicinal analysis is a complex procedure, requiring precise patient data, scientific knowledge obtained over numerous years and a theoretical understanding of related medical literature. To improve the accuracy and to reduce the time for diagnosis, clinical decision support systems (DSS) were introduced, which incorporate data mining schemes for enhancing the disease diagnosing accuracy. This work proposes a new disease-predicting model that involves 3 stages. Initially, “improved stemming and tokenization” are carried out in the pre-processing stage. Then, the “Fuzzy ontology, improved mutual information (MI), and correlation features” are extracted. Then, prediction is carried out via ensemble classifiers that include “improved Fuzzy logic, Long Short Term Memory (LSTM), Deep Convolution Neural Network (DCNN), and Bidirectional Gated Recurrent Unit (Bi-GRU)”.The outcomes from improved fuzzy logic, LSTM, and DCNN are further classified via Bi-GRU which offers the results. Specifically, Bi-GRU weights are optimally tuned using Deer Hunting Update Explored Arithmetic Optimization (DHUEAO). Finally, the efficiency of the proposed work is determined concerning a variety of metrics.