Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection.

Wireless Body Area Network (WBAN) is a trending technology of Wireless Sensor Networks (WSN) to enhance the healthcare system. This system is developed to monitor individuals by observing their physical signals to offer physical activity status as a wearable low-cost system that is considered an unremarkable solution for continuous monitoring of cardiovascular health. Various studies have discussed the uses of WBAN in Personal Health Monitoring systems (PHM) based on real-world health monitoring models. The major goal of WBAN is to offer early and fast analysis of the individuals but it is not able to attain its potential by utilizing conventional expert systems and data mining. Multiple kinds of research are performed in WBAN based on routing, security, energy efficiency, etc. This paper suggests a new heart disease prediction under WBAN. Initially, the standard patient data regarding heart diseases are gathered from benchmark datasets using WBAN. Then, the channel selections for data transmission are carried out through the Improved Dingo Optimizer (IDOX) algorithm using a multi-objective function. Through the selected channel, the data are transmitted for the deep feature extraction process using One Dimensional-Convolutional Neural Networks (ID-CNN) and Autoencoder. Then, the optimal feature selections are done through the IDOX algorithm for getting more suitable features. Finally, the IDOX-based heart disease prediction is done by Modified Bidirectional Long Short-Term Memory (M-BiLSTM), where the hyperparameters of BiLSTM are tuned using the IDOX algorithm. Thus, the empirical outcomes of the given offered method show that it accurately categorizes a patient's health status founded on abnormal vital signs that is useful for providing the proper medical care to the patients.

Heuristic-based channel selection with enhanced deep learning for heart disease prediction under WBAN.

The main intention of this proposal is to design and develop a new heart disease prediction model via WBAN using three stages. The first stage is data aggregation, in which data is scheduled in Time Division Multiple Access manner based on priority level, and the data from the public benchmark datasets are collected representing WBAN. In the second stage, a channel selection is performed using a developed hybrid metaheuristic algorithm named Tunicate Swarm-Sail Fish Optimization (TS-SFO) Algorithm. The main intention of the suggested channel selection algorithm is to solve the multi-objective problem based on certain constraints like Reference Signal Received Quality, Signal to Noise Ratio and channel capacity. The third stage is the heart disease prediction stage, in which the feature extraction and prediction are performed. The data transmitted in the selected channel is used for the feature extraction phase, where the weighted entropy-based statistical feature extraction is developed and extracts the essential statistical features. Then, an enhanced Recurrent Neural Network (RNN) is proposed by tuning certain parameters using the proposed TS-SFO for predicting heart disease with the help of extracted statistical features. Test results show that the flexible design and subsequent tuning of RNN hyper-parameters can achieve a high prediction rate.