VLSI Architectures for Healthcare System using Machine Learning: A Review
Main Article Content
Abstract
The rapid growth of artificial intelligence and machine learning (ML) has significantly influenced healthcare systems, enabling advanced diagnostic and predictive capabilities. However, the computational and energy demands of ML models create challenges for real-time, low-power, and resource-constrained medical devices. To overcome these limitations, Very-Large-Scale Integration (VLSI) architectures are increasingly adopted to accelerate ML algorithms such as convolutional neural networks (CNNs), long short-term memory (LSTM), and gradient boosting models like XGBoost. These architectures enable high-performance computation with reduced latency, power consumption, and silicon area, making them ideal for embedded and wearable healthcare applications. This review highlights recent advancements in VLSI architectures for ML-driven healthcare systems, emphasizing FPGA, ASIC, and hybrid SoC implementations from 2020 to 2025. The study discusses architectural trade-offs, quantization techniques, model compression, and dataflow optimization to improve efficiency. Additionally, emerging trends such as on-chip learning, hardware-aware model design, and secure edge inference are explored. The integration of VLSI with ML in healthcare not only enhances accuracy and speed in medical imaging, biosignal analysis, and disease prediction but also ensures privacy and reliability in edge-based clinical systems.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Pereira, C.V.F.; de Oliveira, E.M.; de Souza, A.D. Machine Learning Applied to Edge Computing and Wearable Devices for Healthcare: Systematic Mapping of the Literature. Sensors 2024, 24, 6322.
O. Taylan, A. Alkabaa, H. Alqabbaa, E. Pamukçu and V. Leiva, "Early prediction in classification of cardiovascular diseases with machine learning neuro-fuzzy and statistical methods", Biology, vol. 12, no. 1, pp. 117, 2023.
R. Bhavani, V. Ramkumar, V. Ravindran, R. Sindhuja and K. Swaminathan, "An efficient SAR image detection based on deep dense-mobile net method", 7th International Conference on Computing in Engineering & Technology (ICCET 2022), vol. 2022, pp. 92-95, 2022, February.
I. Sutedja, "Descriptive and predictive analysis on heart disease with machine learning and deep learning", 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS), pp. 1-6, 2021, October.
Karna Vishnu Vardhana Reddy, Irraivan Elamvazuthi, Azrina Abd Aziz, Sivajothi Paramasivam and Hui Na Chua, “Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis”, International Conference on Intelligent and Advanced Systems (ICIAS), pp. 01-05, IEEE 2021.
P. Rani, R. Kumar, N. Ahmed and A. Jain, "A decision support system for heart disease prediction based upon machine learning", Journal of Reliable Intelligent Environments, vol. 7, pp. 263-275, 2021.
M. Diwakar, A. Tripathi, K. Joshi, M. Memoria, P. Singh and N. Kumar, "Latest trends on heart disease prediction using machine learning and image fusion", Materials Today: Proceedings, vol. 37, pp. 3213-3218, 2021.
M. Ganesan and Dr. N. Sivakumar, “IoT based heart disease prediction and diagnosis model for healthcare using machine learning models”, International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 01-05, IEEE 2019.
Priyan Malarvizhi Kumar, Usha Devi Gandhi, “A novel Internet of Things architecture with machine learning algorithm for early detection of heart diseases", Computers and Electrical Engineering, Vol.65, pp. 222–235, 2018.
Cervantes, J., García-Lamont, F., Rodríguez, L., Lopez-Chau, A., “A comprehensive survey on support vector machine classification: Applications, challenges and trends”, Neurocomputing, Vol. 408, pp. 189–215, 2020.
A. Javeed, S. Rizvi, S. Zhou, R. Riaz, S. Khan and S. Kwon, "Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification", Mobile Information Systems, pp. 1-11, 2020.
C. Latah and S. Jeeva, "Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques", Informatics in Medicine Unlocked 16, pp. 100203, 2019.
C. S. M. Wu, M. Badshah and V. Bhagwat, "Heart disease prediction using data mining techniques", Proceedings of the 2019 2nd international conference on data science and information technology, pp. 7-11, 2019, July.
A. Gavhane, G. Kokkula, I. Pandya and K. Devadkar, "Prediction of heart disease using machine learning", 2018 second international conference on electronics communication and aerospace technology (ICECA), pp. 1275-1278, 2018, March.
Amin Khatami and Abbas Khosravi, “Medical image analysis using wavelet transform and deep belief networks”, Journal of Expert Systems with Applications, Vol. 3, Issue 4, pp. 190–198, 2017.