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| Title | Advancing Remote Healthcare Monitoring: Iot Integration With Xgboost And Bi-Lstm For Enhanced Prediction And Accessibility |
|---|---|
| ID_Doc | 6681 |
| Authors | Murali M.; Pansy D.L. |
| Year | 2025 |
| Published | Smart Healthcare, Clinical Diagnostics, and Bioprinting Solutions for Modern Medicine |
| DOI | http://dx.doi.org/10.4018/979-8-3373-0659-9.ch002 |
| Abstract | IoT technology is playing important role in tracking and communicating data in different fields, including healthcare, smart cities, and others. In IoT-based remote monitoring system, important parameters are transmitted through network. These parameters are accessed to analyse the patient's condition. The methodology adopted is to collect the physiological data for regular patient monitoring. This method enhances the heart disease prediction by analysing the vital parameters. An approach uses machine learning model combined with IoT technology used for heart disease prediction. Specifically, we employ Extreme Gradient Boosting (XGBoost) algorithm to analyse the sensor data and interpret the characteristics to improve the prediction accuracy. The Bidirectional Long Short-Term Memory is used to improve the prediction to extract temporal dependencies from patient data. This technique is compared with other machine learning models like decision tree, naïve bayes and random forest. The proposed model achieved a higher prediction accuracy of 95.3% combining XGBoost and Bi-LSTM. © 2025, IGI Global Scientific Publishing. All rights reserved. |
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