Transforming physical healthcare training through integration of machine learning and advanced artificial intelligent methods
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Abstract
Mental healthcare and heart disease continues to be a major cause of death worldwide, making it essential to find effective ways to prevent it. Physical activity has long been known to be important for preventing and treating mental healthcare disease, but it is not always clear how much and what type of activity an individual should do. Artificial intelligence (AI) models that can predict a person's risk of mental healthcare disease based on their individual characteristics have become increasingly popular in recent years. In this study, we used AI models to explore the relationship between physical activity and mental healthcare using data from 100 participants that included information about their demographics, medical history, lifestyle, and environment. Our dataset encompasses crucial variables like age, gender, ethnicity, medical history (including heart disease and high blood pressure), socioeconomic status, physical activity (including type, duration, intensity, and frequency), and environmental factors. In this study we predict the mental healthcare and physical activities effect using machine learning (LR, DT, RF, SVM and KNN) and deep learning (CNN, RNN, TabNet, GAN and DQN) models. We leverage cutting-edge analytics, such as machine learning and deep learning, to forecast mental healthcare history based on these factors and examine physical activities and mental health impact on healthcare. Our proposed machine learning method, Random Forest, has demonstrated remarkable accuracy of 88%, while the deep learning model, TabNet-Transform, has achieved an impressive accuracy of 90%. Our Model play key role in enhancing mental well-being and controlling certain psychoneurological disorders.
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Anhui University
Grant numbers SK2021A0781 -
Double First Class University Plan
Grant numbers 2024jyxm19
References
Addissouky, T., Sayed, I., Ali, M., Wang, Y., Elbaz, A., Elarabany, N., & Khalil, A. (2024). Shaping the Future of Cardiac Wellness: Exploring Revolutionary Approaches in Disease Management and Prevention. Journal of Clinical Cardiology, 5, 6-29. https://doi.org/10.33696/cardiology.5.048
Adedinsewo, D. A., Pollak, A. W., Phillips, S. D., Smith, T. L., Svatikova, A., Hayes, S. N., Mulvagh, S. L., Norris, C., Roger, V. L., Noseworthy, P. A., Yao, X., & Carter, R. E. (2022). Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools. Circ Res, 130(4), 673-690. https://doi.org/10.1161/CIRCRESAHA.121.319876
Albawi, S., Abed Mohammed, T., & Alzawi, S. (2017). Understanding of a Convolutional Neural Network. https://doi.org/10.1109/ICEngTechnol.2017.8308186
B Padmaja, C. S., Kotha Sindhu, Kalali Vanaja, N M Deepika, E Krishna Rao Patro. (2021). Early and Accurate Prediction of Heart Disease Using Machine Learning Model. . Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6). https://doi.org/10.17762/turcomat.v12i6.8438
Biau, G., & Scornet, E. (2015). A Random Forest Guided Tour. TEST, 25. https://doi.org/10.1007/s11749-016-0481-7
Dey, R., & Salem, F. (2017). Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks. https://doi.org/10.1109/MWSCAS.2017.8053243
Dey, S., Mitra, T., & Nath, T. (2024). 23. Artificial Intelligence and its Application in Mental Health Care. Mosaic of Ideas: Multidisciplinary Reflections, 202.
Fatima, S. (2024). Transforming Healthcare with AI and Machine Learning: Revolutionizing Patient Care Through Advanced Analytics. International Journal of Education and Science Research Review, 11.
Francis, J., Varghese, J., & Thomas, A. (2023). Impact of artificial intelligence on healthcare. International Journal of Advances in Medicine, 10. https://doi.org/10.18203/2349-3933.ijam20232839
Jain, L. C., & Medsker, L. R. (1999). Recurrent Neural Networks: Design and Applications. https://doi.org/10.1201/9781420049176
Kagiyama, N., Shrestha, S., Farjo, P. D., & Sengupta, P. P. (2019). Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease. Journal of the American Heart Association, 8(17), e012788. https://doi.org/10.1161/JAHA.119.012788
Krittanawong, C., Bomback, A. S., Baber, U., Bangalore, S., Messerli, F. H., & Wilson Tang, W. H. (2018). Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension. Curr Hypertens Rep, 20(9), 75. https://doi.org/10.1007/s11906-018-0875-x
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol, 69(21), 2657-2664. https://doi.org/10.1016/j.jacc.2017.03.571
Liu, Q., Long, L., Yang, Q., Peng, H., Wang, J., & Luo, X. (2021). LSTM-SNP: A long short-term memory model inspired from spiking neural P systems. Knowledge-Based Systems, 235, 107656. https://doi.org/10.1016/j.knosys.2021.107656
Lopes, M. H. B. d. M., Ferreira, D. D., Ferreira, A. C. B. H., Silva, G. R. da, Caetano, A. S., & Braz, V. N. (2021). Use of artificial intelligence in precision nutrition and fitness (pp. 465-496). Academic Press. https://doi.org/10.1016/B978-0-12-817133-2.00020-3
Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020). Artificial Intelligence, Machine Learning, and Cardiovascular Disease. Clin Med Insights Cardiol, 14, 1179546820927404. https://doi.org/10.1177/1179546820927404
Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018). Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights into Imaging, 9(5), 745-753. https://doi.org/10.1007/s13244-018-0645-y
Rahman, A., Debnath, T., Kundu, D., Khan, M. S. I., Aishi, A. A., Sazzad, S., Sayduzzaman, M., & Band, S. S. (2024). Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities. AIMS Public Health, 11(1), 58-109. https://doi.org/10.3934/publichealth.2024004
Said, S. A., Habashy, S. M., Salem, S. A., & Saad, E.-S. M. (2022). A Straggler Identification Model for Large-Scale Distributed Computing Systems Using Machine Learning. International Conference on Advanced Intelligent Systems and Informatics. https://doi.org/10.1007/978-3-031-20601-6_10
Sermesant, M., Delingette, H., Cochet, H., Jaïs, P., & Ayache, N. (2021). Applications of artificial intelligence in cardiovascular imaging. Nat Rev Cardiol, 18(8), 600-609. https://doi.org/10.1038/s41569-021-00527-2
Shu, S., Ren, J., & Song, J. (2021). Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases. Circ J, 85(9), 1416-1425. https://doi.org/10.1253/circj.CJ-20-1121
Tiwari, M., & Waoo, A. (2024). Transforming Healthcare: The Synergistic Fusion of AI and IoT for Intelligent, Personalized Well-Being. In (pp. 109-149). https://doi.org/10.1007/978-3-031-65022-2_7
Vajjhala, N. R., & Eappen, P. (2024). Smart Health: Advancements in Machine Learning and the Internet of Things Solutions. In Machine Learning and IoT Applications for Health Informatics (pp. 31-51). CRC Press. https://doi.org/10.1201/9781003424987-3
Vijayakumar, S., Lee, V. V., Leong, Q. Y., Hong, S. J., Blasiak, A., & Ho, D. (2023). Physicians' Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform. JMIR Hum Factors, 10, e48476. https://doi.org/10.2196/48476
Ville, B. (2013). Decision trees. Wiley Interdisciplinary Reviews: Computational Statistics, 5. https://doi.org/10.1002/wics.1278
Yapıcı, M. M., & Topaloğlu, N. (2021). Performance comparison of deep learning frameworks. Computers and Informatics, 1(1), 1-11.