Hsc-attention multi-disease prediction Net: A heuristic-assisted hybrid serial cascaded Attention-based Network with ensemble feature selection process for Multi-Disease prediction

Anish, T.P.; Joe Prathap, P.M.

doi:10.1016/j.bspc.2023.105676

Biomedical Signal Processing and Control

2024

DOI: 10.1016/j.bspc.2023.105676

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Hsc-attention multi-disease prediction Net: A heuristic-assisted hybrid serial cascaded Attention-based Network with ensemble feature selection process for Multi-Disease prediction

T.P. Anish,

P.M. Joe Prathap

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2024

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An efficient multi-disease prediction model using advanced optimization aided weighted convolutional neural network with dilated gated recurrent unit

Satheeskumaran

2024

IDT

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In the medical field, prediction accuracy over multi-diseases is significant and it is helpful for improving the patient’s health. Most of the conventional machine learning techniques concentrates only on detecting single diseases. Only a few systems are developed for predicting more than one disease. The classification of multi-label data is a challenging issue. Patients have symptoms of various diseases while analyzing the medical data and hence it is necessary to implement tools for the earlier identification of problems. The patterns in the health data have been effectively identified through deep learning-based health risk prediction models. Thus, an efficient prediction model for predicting various types of diseases is implemented in this work. Initially, the required data regarding various types of diseases will be gathered from Kaggle database. The garnered healthcare data are pre-processed for quality enhancement. The pre-processing procedures include data cleaning, data transformation, and outlier detection are performed at first. The outlier detection is done using the “Density-Based Spatial Clustering of Applications with Noise (DBSCAN)” approach. The pre-processed data is then given to the Weighted Convolutional Neural Network Feature with Dilated Gated Recurrent Unit (WCNNF-DGRU) model. Here, the pre-processed data is provided to the CNN structure for feature extraction, in which the weights are optimized by means of the Enhanced Kookaburra Optimization Algorithm (EKOA). Then the features from the weighted CNN layer are provided to the Dilated GRU structure to determine the final prediction output. Experimental verification is carried out on the implemented WCDG in predicting multiple diseases by comparing it with other conventional prediction models and optimization algorithms.

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An efficient multi-disease prediction model using advanced optimization aided weighted convolutional neural network with dilated gated recurrent unit

Satheeskumaran

2024

IDT

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Hsc-attention multi-disease prediction Net: A heuristic-assisted hybrid serial cascaded Attention-based Network with ensemble feature selection process for Multi-Disease prediction

Cited by 1 publication

References 16 publications

An efficient multi-disease prediction model using advanced optimization aided weighted convolutional neural network with dilated gated recurrent unit

An efficient multi-disease prediction model using advanced optimization aided weighted convolutional neural network with dilated gated recurrent unit

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