Telehealth Revolution: Leveraging CNN-Bi LSTM For Multiple Disease Prediction

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 1

Year of Publication : 2025

Authors : Divya R Unnithan, J. R. Jeba

10.14445/23488549/IJECE-V12I1P101

How to Cite?

Divya R Unnithan, J. R. Jeba, "Telehealth Revolution: Leveraging CNN-Bi LSTM For Multiple Disease Prediction," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 1, pp. 1-13, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I1P101

Abstract:

Telemedicine has become a key instrument, enabling remote disease diagnosis in the last few years. Patients in underserved areas get access to medical services through telemedicine. Within the telemedicine framework to improve the accuracy of multiple disease prediction, this study presents a hybrid model that integrates a convolutional neural network and bidirectional long short-term memory. The dataset was gathered from the repository of the YBI foundation. This study employs a convolutional neural network to efficiently extract local patterns and features from the input data. Meanwhile, bidirectional long short-term memory captures long-term dependency and temporal patterns by sequentially processing the extracted features. The proposed model attains excellent performance, including 99.04 % recall, 98.99 % F1 score, 98.98 % accuracy, and 99.03 % precision. Compared to current methods, the performance of the suggested methods demonstrates better results and greater efficiency. To improve patient outcomes and healthcare efficiency, the CNN-Bi LSTM model’s potential is high in telemedicine applications, showing how well it predicts various diseases.

Keywords:

Telemedicine, Convolutional Neural Networks, multiple disease prediction, Hybrid model, Bidirectional Long Short-Term Memory.

References:

[1] Abid Haleem et al., “Telemedicine for Healthcare: Capabilities, Features, Barriers, and Applications,” Sensors International, vol. 2, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Nnenna Kalu, “The Use of Health Technology in Emerging Markets: China Telemedicine Market Analysis,” Master Thesis, University of Pittsburgh, 2022.
[Google Scholar] [Publisher Link]
[3] Claudio F. Donner, Richard ZuWallack, and Linda Nici, “The Role of Telemedicine in Extending and Enhancing Medical Management of the Patient with Chronic Obstructive Pulmonary Disease,” Medicina, vol. 57, no. 7, pp. 1-13, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Racha Ftouni et al., “Challenges of Telemedicine During the COVID-19 Pandemic: A Systematic Review,” BMC Medical Informatics and Decision Making, vol. 22, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Yichuan Wang, LeeAnn Kung, and Terry Anthony Byrd, “Big Data Analytics: Understanding Its Capabilities and Potential Benefits for Healthcare Organizations,” Technological Forecasting and Social Change, vol. 126, pp. 3-13, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Sara Yahya Kadum et al., “Machine Learning-based Telemedicine Framework to Prioritize Remote Patients with Multi-Chronic Diseases for Emergency Healthcare Services,” Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Hossam Faris et al., “An Intelligent Multimodal Medical Diagnosis System based on Patients’ Medical Questions and Structured Symptoms for Telemedicine,” Informatics in Medicine Unlocked, vol. 23, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Chia-Tung Wu et al., “A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine Learning, and Deep Learning,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 10, pp. 1-14, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Parameshwar Reddy Kothamali, “Advancing Telemedicine and Healthcare Systems with AI and Machine Learning,” International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, vol. 15, no. 1, pp. 177-207, 2024.
[Publisher Link]
[10] Maria Habib et al., “Toward an Automatic Quality Assessment of Voice-based Telemedicine Consultations: A Deep Learning Approach,” Sensors, vol. 21, no. 9, pp. 1-26, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Kashif Hameed et al., “Integration of 5G and Block‐Chain Technologies in Smart Telemedicine Using IoT,” Journal of Healthcare Engineering, vol. 2021, no. 1, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Lu Men et al., “Multi-disease Prediction using LSTM Recurrent Neural Networks,” Expert Systems with Applications, vol. 177, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] A.S. Prakaash et al., “Design and Development of Modified Ensemble Learning with Weighted RBM Features for Enhanced Multi-disease Prediction Model,” New Generation Computing, vol. 40, pp. 1241-1279, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Anusha Ampavathi, and T. Vijaya Saradhi, “Multi Disease-prediction Framework using Hybrid Deep Learning: An Optimal Prediction Model,” Computer Methods in Biomechanics and Biomedical Engineering, vol. 24, no. 10, pp. 1146-1168, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Tingyan Wang, Yuanxin Tian, and Robin G. Qiu, “Long Short-Term Memory Recurrent Neural Networks for Multiple Diseases Risk Prediction by Leveraging Longitudinal Medical Records,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 8, pp. 2337-2346, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Kenya Murata et al., “A Single Filter CNN Performance for Basic Shape Classification,” 2018 9th International Conference on Awareness Science and Technology (iCAST), Fukuoka, Japan, pp. 139-143, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Sima Siami-Namini, Neda Tavakoli, and Akbar Siami Namin, “The Performance of LSTM and BiLSTM in Forecasting Time Series,” 2019 IEEE International Conference on Big Data (Big Data), pp. 3285-3292, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Dhiraj Dahiwade, Gajanan Patle, and Ektaa Meshram, “Designing Disease Prediction Model using Machine Learning Approach,” 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, pp. 1211-1215, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Pahulpreet Singh Kohli, and Shriya Arora, “Application of Machine Learning in Disease Prediction,” 2018 4th International conference on Computing Communication and Automation (ICCCA), Greater Noida, India, pp. 1-4, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Zvi Segal et al., “Machine Learning Algorithm for Early Detection of End-Stage Renal Disease,” BMC Nephrology, vol. 21, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Mohamed Elhoseny, K. Shankar, and J. Uthayakumar, “Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease,” Scientific Reports, vol. 9, pp. 1-14, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Ibomoiye Domor Mienye, Yanxia Sun, and Zenghui Wang, “An Improved Ensemble Learning Approach for the Prediction of Heart Disease Risk,” Informatics in Medicine Unlocked, vol. 20, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Achyut Tiwari, Aryan Chugh, and Aman Sharma, “Ensemble Framework for Cardiovascular Disease Prediction,” Computers in Biology and Medicine, vol. 146, 2022.
[CrossRef] [Google Scholar] [Publisher Link]