RETRACTED ARTICLE: An effective disease prediction system using incremental feature selection and temporal convolutional neural network

An IoT Enabled Heart Disease Monitoring System Using Grey Wolf Optimization and Deep Belief Network

“…Figure 5 shows the performance based comparative analysis of the proposed IoT enabled health monitoring system with the consideration of different heart disease affected datasets that are collected through IoT devices and also compared with the existing prediction systems including Temporal convolutional neural network [2], new multimodal data-based recurrent convolutional neural network (MD-RCNN) [12], Fuzzy Temporal Cognitive Map (FTCM) [3] and Fuzzy Rule based Classifier [27]. From figure 5, it is seen that the performance of the proposed health monitoring system with that prediction accuracy is high and it is almost equal to the sensitivity and the specificity.…”

“…They have achieved 95% prediction accuracy. Sandhiya et al [2] proposed an disease prediction system which consists of feature selection method that works as incremental in nature named as Incremental Feature Selection Algorithm (IFSA) which combines the concepts of Intelligent Conditional Random Field (CRF) on feature selection process and the Linear Correlation Coefficient based Feature Selection (ICRF-LCFS) method algorithm and an existing Convolutional Neural Network (CNN) with temporal features (T-CNN). Sethukkarasi et al [3] proposed a new fuzzy cognitive map based intelligent disease prediction system with the consideration of temporal constraints for predicting the heart disease, breast cancer disease and diabetic disease.…”

Efficient heart disease prediction‐based on optimal feature selection using DFCSS and classification by improved Elman‐SFO

“…This disease makes dangerous complexity, such as heart attack and death. In this paper, an efficient heart disease prediction based on optimal FS [26] fuzzy rules + DT UCI 88 Ibrahim and Sivabalakrishnan [27] Evolutionary memetic weighted associative classification Algorithm (EMWACA) UCI 95.4 Thiyagaraj and Suseendran [28] RBF-TSVM UCI 97 Maji and Arora [38] hybrid DT UCI 78 Paul et al [39] adaptive fuzzy diagnostic support system (FDSS) UCI 92 Reddy and Khare [17] Rule based fuzzy logic (RBFL) UCI 78 Magesh and Swarnalatha [22] CDTL-RF UCI 89.30 Sandhiya and Palani [23] T-CNN UCI 85.4 Babu and Shantharajah [24] GA-SVM UCI 88.34 Jayaraman and Sultana [25] AGCS-PBAMNN UCI 99.8…”

“…The combination of CDTL-RF achieved enhanced prediction accuracy (89.03%). Sandhiya and Palani [23] introduced a FS-based disease prediction system. The FS approach used was incremental FS algorithm (IFSA).…”

Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

“…Masking techniques & Binarization are matched for image feature extraction, but binarization method provides better results. Computer aided identification technique on the basis of hierarchical vector quantization scheme has been suggested by Authors [9] & it offers more precise segmentation compared to threshold method. SVM Classifier approach is utilized here, providing 92.7 percent sensitivity and 93.3 percent specificity and 82.7% overall sensitivity at 4FP/Scan.…”