DISEASE RISK PREDICTION USING ELECTRONIC HEALTH RECORD DATA BASED ON FLYING SQUIRREL SEARCH OPTIMIZATION WITH BIDIRECTIONAL – LONG SHORT TERM MEMORY

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Bibliographic Details
Main Author:	Journal of Theoretical and Applied Information Technology
Format:	Recurso digital
Language:	English
Published:	Zenodo 2025
Subjects:	Bi-Directional Long Short Term Memory, Disease Risk Prediction, Electronic Health Record, Flying Squirrel Search Optimization, Machine Learning.
Online Access:	Acceder al recurso
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author	Journal of Theoretical and Applied Information Technology
author_facet	Journal of Theoretical and Applied Information Technology
contents	<p><span>Nowadays, the attention towards effective disease risk prediction has increased, due to its importance in monitoring the future health status of patients. This prediction helps to provide the right treatment for patients to prevent severe stages of diseases. However, the existing risk prediction models based on Machine Learning (ML) have limitations in learning temporal information from the Electronic Health Record (EHR) data. To overcome this, a Flying Squirrel Search Optimization (FSSO) algorithm for feature selection and Bi-directional Long Short Term Memory (Bi-LSTM) is proposed to enhance the accurate disease risk prediction using EHR data. The proposed FSSO based feature selection method efficiently reduces the dimensionality of EHR data and helps to identify the most predictive features for disease risk accurately. By utilizing the bidirectional layers, Bi-LSTM model learns the dependencies in both past and future directions, which makes the model suitable for capturing comprehensive temporal patterns that influence disease risk. Initially, the EHR data is acquired and preprocessed by solving the missing values in the dataset to enhance the risk prediction process. Experimental results of the proposed method achieved an accuracy of 0.938 for MIMIC-IV dataset when compared to existing methods such as XGBoost and RDF.</span></p>
format	Recurso digital
id	zenodo_https___doi_org_10_5281_zenodo_17203304
institution	Zenodo
language	eng
publishDate	2025
publisher	Zenodo
record_format	zenodo
spellingShingle	DISEASE RISK PREDICTION USING ELECTRONIC HEALTH RECORD DATA BASED ON FLYING SQUIRREL SEARCH OPTIMIZATION WITH BIDIRECTIONAL – LONG SHORT TERM MEMORY Journal of Theoretical and Applied Information Technology Bi-Directional Long Short Term Memory, Disease Risk Prediction, Electronic Health Record, Flying Squirrel Search Optimization, Machine Learning. <p><span>Nowadays, the attention towards effective disease risk prediction has increased, due to its importance in monitoring the future health status of patients. This prediction helps to provide the right treatment for patients to prevent severe stages of diseases. However, the existing risk prediction models based on Machine Learning (ML) have limitations in learning temporal information from the Electronic Health Record (EHR) data. To overcome this, a Flying Squirrel Search Optimization (FSSO) algorithm for feature selection and Bi-directional Long Short Term Memory (Bi-LSTM) is proposed to enhance the accurate disease risk prediction using EHR data. The proposed FSSO based feature selection method efficiently reduces the dimensionality of EHR data and helps to identify the most predictive features for disease risk accurately. By utilizing the bidirectional layers, Bi-LSTM model learns the dependencies in both past and future directions, which makes the model suitable for capturing comprehensive temporal patterns that influence disease risk. Initially, the EHR data is acquired and preprocessed by solving the missing values in the dataset to enhance the risk prediction process. Experimental results of the proposed method achieved an accuracy of 0.938 for MIMIC-IV dataset when compared to existing methods such as XGBoost and RDF.</span></p>
title	DISEASE RISK PREDICTION USING ELECTRONIC HEALTH RECORD DATA BASED ON FLYING SQUIRREL SEARCH OPTIMIZATION WITH BIDIRECTIONAL – LONG SHORT TERM MEMORY
topic	Bi-Directional Long Short Term Memory, Disease Risk Prediction, Electronic Health Record, Flying Squirrel Search Optimization, Machine Learning.
url	https://doi.org/10.5281/zenodo.17203304