3066-280XISSN (Online)
Volume 2 , Issue 1 , PP: 01-13, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Marwa Radwan 1 * , Shomona Jacob 2
Doi: https://doi.org/10.54216/MOR.020101
The present review aims to describe the impact of machine learning techniques in health risk prediction, including the progress, drawbacks, and potential development. ML approaches in health care have become more effective in risk prediction than simple regression techniques because of their accuracy, scalability, and personalization. A Statistical tool like Decision trees, Support vector machines, and neural networks allows or examine non-linear genetic and environmental interactions with lifestyle factors. The review's main points are the increase in relevance of more complex types of models like ANN-PSO, a combination of two algorithms for feature selection, higher prediction accuracy, and other fields, including healthcare. These innovations have shown a unique success rate in identifying diseases, including obesity, diabetes, and any cardiovascular diseases, for better prevention measures and avenues of cure. Nevertheless, there are several difficulties: inferior quality of the data, the question of privacy, and explaining the decision-making of the modern complex models. Solving these issues calls for effective data governance, explainable artificial intelligence, and a multi-disciplinary approach to create and deliver transparency and fairness. As mentioned in the review, feature importance analysis like SHAP also carries plenty of significance for enhancing model interpretability to chase positive alterations. Concerning the outlook, implementing ML in the current HC system will require investment in data platforms, clinician expertise, and broader, suitable systems. As a result, new opportunities for using ML in connection with population health, patient and client outcomes, and receiving individualized care point out the further evolution of the transforming impact of technology. This paper offers an understanding of how health risk prediction and the public health strategy might benefit from new applications of ML and how the moral and practical issues of this new application of the technology may be dealt with.
Machine learning , Health risk prediction , Hybrid models , Public health , Data privacy , explainable AI
[1] Maciej Serda et al., “Synteza i aktywność biologiczna nowych analogów tiosemikarbazonowych chelatorów żelaza,” Uniwersytet śląski, vol. 7, no. 1, pp. 343–354, 2013, doi: 10.2/JQUERY.MIN.JS.
[2] P. Gupta, B. Ding, C. Guan, and D. Ding, “Generative AI: A systematic review using topic modelling techniques,” Data Inf Manag, vol. 8, no. 2, p. 100066, Jun. 2024, doi: 10.1016/J.DIM.2024.100066.
[3] S. E. Abhadiomhen, E. O. Nzeakor, and K. Oyibo, “Health Risk Assessment Using Machine Learning: Systematic Review,” Electronics (Switzerland), vol. 13, no. 22, p. 4405, Nov. 2024, doi: 10.3390/ELECTRONICS13224405/S1.
[4] A. S. Tang et al., “Leveraging electronic health records and knowledge networks for Alzheimer’s disease prediction and sex-specific biological insights,” Nature Aging 2024 4:3, vol. 4, no. 3, pp. 379–395, Feb. 2024, doi: 10.1038/s43587-024-00573-8.
[5] Y. Zhao, E. P. Wood, N. Mirin, S. H. Cook, and R. Chunara, “Social Determinants in Machine Learning Cardiovascular Disease Prediction Models: A Systematic Review,” Am J Prev Med, vol. 61, no. 4, pp. 596–605, Oct. 2021, doi: 10.1016/J.AMEPRE.2021.04.016.
[6] R. van den Berg, T. N. Kipf, and M. Welling, “Graph Convolutional Matrix Completion,” Jun. 2017, Accessed: Dec. 05, 2024. [Online]. Available: https://arxiv.org/abs/1706.02263v2
[7] S. H. Jacobson and J. A. Jokela, “Artificial Intelligence and Public Health,” Artificial Intelligence for Healthcare, pp. 3–12, Apr. 2022, doi: 10.1017/9781108872188.003.
[8] A. P. Zhao et al., “AI for science: Predicting infectious diseases,” Journal of Safety Science and Resilience, vol. 5, no. 2, pp. 130–146, Jun. 2024, doi: 10.1016/J.JNLSSR.2024.02.002.
[9] M. Alruqimi and L. Di Persio, “Multistep Brent oil price forecasting with a multi-aspect aeta-heuristic optimization and ensemble deep learning model,” Energy Informatics 2024 7:1, vol. 7, no. 1, pp. 1–19, Nov. 2024, doi: 10.1186/S42162-024-00421-4.
[10] A. Agrawal and A. Jain, “Brhamo: metaheuristic optimization algorithm for speech emotion recognition using spectral and hybrid features,” Evol Intell, vol. 18, no. 1, pp. 1–20, Feb. 2025, doi: 10.1007/S12065-024-00994-9/METRICS.
[11] H. Dihmani, A. Bousselham, and O. Bouattane, “A New Computer-Aided Diagnosis System for Breast Cancer Detection from Thermograms Using Metaheuristic Algorithms and Explainable AI,” Algorithms 2024, Vol. 17, Page 462, vol. 17, no. 10, p. 462, Oct. 2024, doi: 10.3390/A17100462.
[12] U. Mumtahina, S. Alahakoon, and P. Wolfs, “Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review,” Mathematics 2024, Vol. 12, Page 3353, vol. 12, no. 21, p. 3353, Oct. 2024, doi: 10.3390/MATH12213353.
[13] A. Punitha and V. Geetha, “Gorilla troops optimization with deep learning based crop recommendation and yield prediction,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 494–504, Jan. 2024, doi: 10.1016/J.IJCCE.2024.09.006.
[14] S. T. Abd Al-Latief, S. Yussof, A. Ahmad, S. M. Khadim, and R. A. Abdulhasan, “Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning,” International Journal of Networked and Distributed Computing, vol. 12, no. 2, pp. 344–361, Dec. 2024, doi: 10.1007/S44227-024-00039-8/FIGURES/12.
[15] S. A. Sharaf et al., “Advanced mathematical modeling of mitigating security threats in smart grids through deep ensemble model,” Scientific Reports 2024 14:1, vol. 14, no. 1, pp. 1–22, Oct. 2024, doi: 10.1038/s41598-024-74733-6.
[16] S. Saifullah and R. Dreżewski, “Automatic Brain Tumor Segmentation Using Convolutional Neural Networks: U-Net Framework with PSO-Tuned Hyperparameters,” pp. 333–351, 2024, doi: 10.1007/978-3-031-70071-2_21/TABLES/4.
[17] M. E. Hosney, E. H. Houssein, M. R. Saad, N. A. Samee, M. M. Jamjoom, and M. M. Emam, “Efficient bladder cancer diagnosis using an improved RIME algorithm with Orthogonal Learning,” Comput Biol Med, vol. 182, p. 109175, Nov. 2024, doi: 10.1016/J.COMPBIOMED.2024.109175.
[18] A. S. Almuflih et al., “Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier,” Scientific Reports 2024 14:1, vol. 14, no. 1, pp. 1–22, Nov. 2024, doi: 10.1038/s41598-024-80255-y.
[19] A. Ciran, S. Ertem, and E. Ozbay, “Optimization-Based Hyperparameter Selection in Deep Learning Methods for Detection of Lung Diseases,” 8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024, 2024, doi: 10.1109/IDAP64064.2024.10710803.
[20] Y. Ahmed et al., “A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent,” J Environ Manage, vol. 370, p. 122614, Nov. 2024, doi: 10.1016/J.JENVMAN.2024.122614.
[21] M. F. Gul and H. Bakir, “Improving Attack Detection in IoV Systems using GA-based Hyperparameter Optimization,” 8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024, 2024, doi: 10.1109/IDAP64064.2024.10711086.
[22] X. Guo, T. Liu, and Q. Chi, “Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization,” Scientific Reports 2024 14:1, vol. 14, no. 1, pp. 1–20, Nov. 2024, doi: 10.1038/s41598-024-77523-2.
[23] A. M. Khan, E. Ugarte, A. BinZiad, and A. Alsubaii, “Physics-Informed Machine Learning for Hydraulic Fracturing—Part II: The Transfer Learning Experiment,” ADIPEC, Nov. 2024, doi: 10.2118/222903-MS.
[24] A. B. Kathole, D. Jadhav, K. N. Vhatkar, S. Amol, and N. Gandhewar, “Solar energy prediction in IoT system based optimized complex-valued spatio-temporal graph convolutional neural network,” Knowl Based Syst, vol. 304, p. 112400, Nov. 2024, doi: 10.1016/J.KNOSYS.2024.112400.
[25] S. T. Abd Al-Latief, S. Yussof, A. Ahmad, S. M. Khadim, and R. A. Abdulhasan, “Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning,” International Journal of Networked and Distributed Computing, vol. 12, no. 2, pp. 344–361, Dec. 2024, doi: 10.1007/S44227-024-00039-8/FIGURES/12.
[26] Y. Ahmed et al., “A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent,” J Environ Manage, vol. 370, p. 122614, Nov. 2024, doi: 10.1016/J.JENVMAN.2024.122614.
[27] A. Agrawal and A. Jain, “Brhamo: metaheuristic optimization algorithm for speech emotion recognition using spectral and hybrid features,” Evol Intell, vol. 18, no. 1, pp. 1–20, Feb. 2025, doi: 10.1007/S12065-024-00994-9/METRICS.
[28] Z. Helforoush and H. Sayyad, “Prediction and classification of obesity risk based on a hybrid metaheuristic machine learning approach,” Front Big Data, vol. 7, p. 1469981, Sep. 2024, doi: 10.3389/FDATA.2024.1469981/BIBTEX.
[29] M. Kavitha, G. Gnaneswar, R. Dinesh, Y. R. Sai, and R. S. Suraj, “Heart Disease Prediction using Hybrid machine Learning Model,” Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021, pp. 1329–1333, Jan. 2021, doi: 10.1109/ICICT50816.2021.9358597.
[30] F. Li, Y. Chen, and H. Xu, “Coronary heart disease prediction based on hybrid deep learning,” Review of Scientific Instruments, vol. 95, no. 1, Jan. 2024, doi: 10.1063/5.0172368/3147153.
[31] M. González-Del-Hoyo and X. Rossello, “Challenges and promises of machine learning-based risk prediction modelling in cardiovascular disease,” Eur Heart J Acute Cardiovasc Care, vol. 10, no. 8, pp. 866–868, Oct. 2021, doi: 10.1093/EHJACC/ZUAB074.
[32] M. Kavitha, G. Gnaneswar, R. Dinesh, Y. R. Sai, and R. S. Suraj, “Heart Disease Prediction using Hybrid machine Learning Model,” Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021, pp. 1329–1333, Jan. 2021, doi: 10.1109/ICICT50816.2021.9358597.
[33] M. González-Del-Hoyo and X. Rossello, “Challenges and promises of machine learning-based risk prediction modelling in cardiovascular disease,” Eur Heart J Acute Cardiovasc Care, vol. 10, no. 8, pp. 866–868, Oct. 2021, doi: 10.1093/EHJACC/ZUAB074.
[34] M. Kavitha, G. Gnaneswar, R. Dinesh, Y. R. Sai, and R. S. Suraj, “Heart Disease Prediction using Hybrid machine Learning Model,” Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021, pp. 1329–1333, Jan. 2021, doi: 10.1109/ICICT50816.2021.9358597.
