Citation:

Mihir Patel, Vandana Chaturvedi, 2025 "A Survey on Artificial Intelligence Techniques for Disease Prediction in Healthcare", ESP Journal of Engineering & Technology Advancements  5(4): 201-210.

Abstract:

The rapid advancement of medical data and the ever-increasing need for effective healthcare services are causing a major shift in the healthcare industry. Clinical reports, wearable devices, medical imaging, and electronic health records all generate massive amounts of data that traditional data analysis tools may struggle to process. Artificial intelligence (AI) is a powerful technology that may help medical professionals analyze complex healthcare data and improve the quality of diagnosis and treatment. In particular, the use of AI, which encompasses ML, DL, and NLP, has shown immense promise in the realm of disease prediction and the discovery of previously unseen patterns in patient data. Early detection of cardiovascular disease, diabetes, cancer, and neurological problems also allows for better treatment results and more timely decision-making. This in-depth analysis of AI's role in healthcare covers a wide range of disease prediction algorithms, including CNNs, SVMs, Decision Trees, RF, and ANN. Integration of AI with the internet of things (AIoT) for health monitoring is also included in the research, along with diagnosis and prognosis. It highlights the fundamental challenges of data quality, privacy, and explain ability of AIoT models in healthcare equipment.

References:

[1] N. Ghaffar Nia, E. Kaplanoglu, and A. Nasab, “Evaluation of artificial intelligence techniques in disease diagnosis and prediction,” Discov. Artif. Intell., vol. 3, no. 1, p. 5, 2023, doi: 10.1007/s44163-023-00049-5.

[2] R. Alkhanbouli, H. Matar Abdulla Almadhaani, F. Alhosani, and M. C. E. Simsekler, “The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions,” BMC Med. Inform. Decis. Mak., vol. 25, no. 1, p. 110, Mar. 2025, doi: 10.1186/s12911-025-02944-6.

[3] I. D. Mienye et al., “A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges,” Informatics Med. Unlocked, vol. 51, p. 101587, 2024, doi: 10.1016/j.imu.2024.101587.

[4] S. Aijazuddin, “Artificial Intelligence in the Diagnosis of Disease: An Analytical Review on the Current Trend in Research Leading to Better Outcomes,” Prem. J. Artif. Intell., Oct. 2024, doi: 10.70389/PJAI.100004.

[5] A. Chaddad, J. Peng, J. Xu, and A. Bouridane, “Survey of Explainable AI Techniques in Healthcare,” Sensors, vol. 23, no. 2, p. 634, Jan. 2023, doi: 10.3390/s23020634.

[6] J. Bajwa, U. Munir, A. Nori, and B. Williams, “Artificial intelligence in healthcare: transforming the practice of medicine,” Futur. Healthc. J., vol. 8, no. 2, Jul. 2021, doi: 10.7861/fhj.2021-0095.

[7] F. Jiang et al., “Artificial intelligence in healthcare: past, present and future,” Stroke Vasc. Neurol., vol. 2, no. 4, pp. 230-243, Dec. 2017, doi: 10.1136/svn-2017-000101.

[8] M. Y. Shaheen, “Applications of Artificial Intelligence (AI) in healthcare: A review,” Sep. 2021. doi: 10.14293/S21991006.1.SOR-.PPVRY8K.v1.

[9] S. Quazi, R. P. Saha, and M. K. Singh, “Applications of Artificial Intelligence in Healthcare,” J. Exp. Biol. Agric. Sci., vol. 10, no. 1, pp. 211–226, Feb. 2022, doi: 10.18006/2022.10(1).211.226.

[10] Ó. Díaz, J. A. R. Dalton, and J. Giraldo, “Artificial Intelligence: A Novel Approach for Drug Discovery,” Trends Pharmacol. Sci., vol. 40, no. 8, pp. 550–551, Aug. 2019, doi: 10.1016/j.tips.2019.06.005.

[11] N. Mohamed, L. Sridhara Rao, M. Sharma, Sureshbaburajasekaranl, Badriasulaimanalfurhood, and S. Kumar Shukla, “In-depth review of integration of AI in cloud computing,” in 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023, 2023. doi: 10.1109/ICACITE57410.2023.10182738.

[12] C. Tayal, “Secure Data Integration Frameworks for Omni-channel Healthcare Marketing Systems,” Int. J. Eng. Res. Technol., vol. 14, no. 10, 2025, doi: 10.5281/zenodo.18080743.

[13] D. C. Angus, “Randomized Clinical Trials of Artificial Intelligence,” JAMA, vol. 323, no. 11, p. 1043, Mar. 2020, doi: 10.1001/jama.2020.1039.

[14] P. Patil and K. Patil, “A Review on Disease Prediction Using Artificial Intelligence,” J. Electr. Comput. Exp., vol. 1, no. 1, pp. 1–10, May 2023, doi: 10.59535/ece.v1i1.8.

[15] F. Deeba and S. R. Patil, “Implementation of Artificial Intelligence in Disease Prediction and Healthcare System- A Survey,” in 2021 Innovations in Power and Advanced Computing Technologies (i-PACT), IEEE, Nov. 2021, pp. 1–8. doi: 10.1109/i-PACT52855.2021.9696698.

[16] D. K. Soumya, G. Sukanya, J. Madhurima, J. A. Reddy, K. Vyshnavi, and D. S. P. Setti, “Ai-Based Disease Detection Using Machine Learning And Convolutional Neural Networks,” Int. Res. J. Eng. Technol., vol. 12, no. 03, pp. 781–787, 2025, doi: 10.13140/RG.2.2.26783.47522.

[17] M. Fatima and M. Pasha, “Survey of Machine Learning Algorithms for Disease Diagnostic,” J. Intell. Learn. Syst. Appl., vol. 09, no. 01, pp. 1–16, 2017, doi: 10.4236/jilsa.2017.91001.

[18] P. Shinde, M. Sanghavi, and T. A. Tran, “A Survey on Machine Learning Techniques for Heart Disease Prediction,” SN Comput. Sci., vol. 6, no. 4, p. 334, Apr. 2025, doi: 10.1007/s42979-025-03860-2.

[19] S. Kaur et al., “Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives,” IEEE Access, vol. 8, pp. 228049–228069, 2020, doi: 10.1109/ACCESS.2020.3042273.

[20] R. Rudra, R. Lingam, S. K. Ravva, and S. A, “A Generalized Deep Learning Approach for Cross-Crop Plant Disease Detection Using the Plant Village Dataset,” J. Mach. Comput., vol. 5, no. 3, pp. 1592–1605, Jul. 2025, doi: 10.53759/7669/jmc202505126.

[21] A. A. Malibari, “An efficient IoT-Artificial intelligence-based disease prediction using lightweight CNN in healthcare system,” Meas. Sensors, vol. 26, p. 100695, Apr. 2023, doi: 10.1016/j.measen.2023.100695.

[22] J. W. Sajja and G. B. Komarina, “Enhancing compliance and data integrity in life sciences and healthcare with S/4HANA: A data management and governance framework,” World J. Adv. Eng. Technol. Sci., vol. 15, no. 2, pp. 2816-2827, May 2025, doi: 10.30574/wjaets.2025.15.2.0843.

[23] O. Hamza and S. Farah, “Disease prediction using NLP techniques,” ITM Web Conf., vol. 69, p. 03001, Dec. 2024, doi: 10.1051/itmconf/20246903001.

[24] T. Shimazaki, D. Anzai, K. Watanabe, A. Nakajima, M. Fukuda, and S. Ata, “Heat Stroke Prevention in Hot Specific Occupational Environment Enhanced by Supervised Machine Learning with Personalized Vital Signs,” Sensors, vol. 22, no. 1, p. 395, Jan. 2022, doi: 10.3390/s22010395.

[25] J. Min, M. Cai, C. Gou, C. Xiong, and X. Yao, “Fusion of forehead EEG with machine vision for real-time fatigue detection in an automatic processing pipeline,” Neural Comput. Appl., Jun. 2022, doi: 10.1007/s00521-022-07466-0.

[26] R. P. Hirten et al., “Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers,” JAMIA Open, vol. 5, no. 2, Apr. 2022, doi: 10.1093/jamiaopen/ooac041.

[27] X. Ding, X. Yue, R. Zheng, C. Bi, D. Li, and G. Yao, “Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data,” J. Affect. Disord., vol. 251, pp. 156–161, May 2019, doi: 10.1016/j.jad.2019.03.058.

[28] J. Kawahara et al., “BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment,” Neuroimage, vol. 146, pp. 1038–1049, Feb. 2017, doi: 10.1016/j.neuroimage.2016.09.046.

[29] L. He et al., “Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants,” Front. Neurosci., vol. 15, Oct. 2021, doi: 10.3389/fnins.2021.753033.

[30] L. Cheng, K. R. Varshney, and H. Liu, “Socially Responsible AI Algorithms: Issues, Purposes, and Challenges,” J. Artif. Intell. Res., vol. 71, pp. 1137–1181, Aug. 2021, doi: 10.1613/jair.1.12814.

[31] G. Prabaharan, S. M. Udhaya Sankar, V. Anusuya, K. Jaya Deepthi, R. Lotus, and R. Sugumar, “Optimized disease prediction in healthcare systems using HDBN and CAEN framework,” MethodsX, vol. 14, p. 103338, Jun. 2025, doi: 10.1016/j.mex.2025.103338.

[32] M. bin Q. Al-Asiri and A. A. Al-Asmari, “Disease Prediction System using Data Mining Techniques based on Classification Mechanism: Survey Study,” J. Pioneer. Med. Sci., vol. 13, no. 4, pp. 25–31, Jul. 2024, doi: 10.61091/jpms202413404.

[33] K. Patel et al., “A survey on artificial intelligence techniques for chronic diseases: open issues and challenges,” Artif. Intell. Rev., vol. 55, no. 5, pp. 3747–3800, Jun. 2022, doi: 10.1007/s10462-021-10084-2.

[34] A. Mondal, A. Ghosh, S. Sardar, and J. Adhikary, “A Literature Survey on Performance Analysis of Machine Learning Algorithms for the Prediction of Diseases,” BKGC Sch., vol. 3, no. 2, pp. 31–35, 2022.

Keywords:

AI Techniques, Healthcare Industry, Disease Prediction, Deep Learning, Patient Care, Physical Health.