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

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.