AI and Data Analytics for Proactive Healthcare Risk Management
DOI:
https://doi.org/10.52783/jns.v14.2607Keywords:
Machine Learning, Diabetes Prediction, Data Analytics, Predictive Models, Healthcare ManagementAbstract
This work aims at examining the use of AI in anticipation of diabetes and the use of data analysis. To achieve this, we trained different classifiers using a broad Diabetes Detection Dataset which includes the following; Logistic Regression, K Nearest Neighbor (KNN), Random Forest Classifier, and Decision Tree Classifier. The observation made from the analysis showed that the Random Forest Classifier yielded the highest overal accuracy of 96. It was found that 82% of the population was affected, with a precision of 0.94 and a recall of 0.69 for positive cases. The overall performance of KNN model was also impressive it had a precision of 0.91 and a recall of 0.62, while LR and DTC gave useful information about the data but did not perform so well in some of the evaluation measurements. Some of these items included correlation heatmaps and ROC curves that helped in capturing the relationship between diabetes and other health aspects such as blood glucose level, HbA1c and model performance. It confirms that the concept enshrined in the AI-technologies actually has a huge potential in the early diagnosis and intervention programs that will eventually lead to efficiency enhancement and harmonization of health care services delivery. Future research should hence focus on issues to do with the generalizations of the models as well as ways of combining data in order to enhance the level of predictive and health care improvement. This study predicts diabetes with the aid of machine learning models. Exploratory data analysis shows that the major predictors of diabetes are age, blood glucose, and hypertension. The implementation of the models Logistic Regression, KNN, Random Forest, and Decision Tree was done. Random Forest turned out to be the best model being very accurate and precise in predicting negative cases and quite reasonable in performance for positive case detection. This research contributes to the early detection of diabetes and potentially better treatment for patients.
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Gadde, S.S. and Kalli, V.D.R., 2020. Descriptive analysis of machine learning and its application in healthcare. Int J Comp Sci Trends Technol, 8(2), pp.189-196.
Jayatilake, S.M.D.A.C. and Ganegoda, G.U., 2021. Involvement of machine learning tools in healthcare decision making. Journal of healthcare engineering, 2021(1), p.6679512.
ABHISHEK, C., CHOUBEY, S.B., PRAFULL, K., DAULATABAD, V.S. and NITIN, J., 2024. Healthcare Transformation: Artificial Intelligence Is the Dire Imperative of the Day. Cureus, 16(6),.
ARJMANDNIA, F. and ALIMOHAMMADI, E., 2024. The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review. Patient Safety in Surgery, 18, pp. 1-6.
BHAGAT, S.V. and DEEPIKA, K., 2024. Navigating the Future: The Transformative Impact of Artificial Intelligence on Hospital Management- A Comprehensive Review. Cureus, 16(2),.
CHANDRA, P., DUBEY, A., SHARMA, S.K. and KARSOLIYA, S., 2024. A novel Conceptualization of AI Literacy and Empowering Employee Experience at Digital Workplace Using Generative AI and Augmented Analytics: A Survey. Journal of Electrical Systems, 20(2), pp. 2582-2603.
GIUFFRÈ, M. and SHUNG, D.L., 2023. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. NPJ Digital Medicine, 6(1), pp. 186.
JEE, Y.K., HASAN, A., KELLOGG, K.C., RATLIFF, W., MURRAY, S.G., SURESH, H., VALLADARES, A., SHAW, K., TOBEY, D., VIDAL, D.E., LIFSON, M.A., PATEL, M., INIOLUWA, D.R., GAO, M., KNECHTLE, W., TANG, L., BALU, S. and SENDAK, M.P., 2024. Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities. PLOS Digital Health, 3(5),.
KALOGIANNIDIS, S., KALFAS, D., PAPAEVANGELOU, O., GIANNARAKIS, G. and CHATZITHEODORIDIS, F., 2024. The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece. Risks, 12(2), pp. 19.
KARRAS, A., GIANNAROS, A., KARRAS, C., THEODORAKOPOULOS, L., MAMMASSIS, C.S., KRIMPAS, G.A. and SIOUTAS, S., 2024. TinyML Algorithms for Big Data Management in Large-Scale IoT Systems. Future Internet, 16(2), pp. 42.
LUKKIEN, D.R.M., STOLWIJK, N.E., ASKARI, S.I., HOFSTEDE, B.M., HENK, H.N., BOON, W.P.C., PEINE, A., MOORS, E.H.M. and MINKMAN, M.M.N., 2024. AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation. JMIR Nursing, 7.
LUO, Y., MAO, C., SANCHEZ-PINTO, L., AHMAD, F.S., NAIDECH, A., RASMUSSEN, L., PACHECO, J.A., SCHNEIDER, D., MITHAL, L.B., DRESDEN, S., HOLMES, K., CARSON, M., SHAH, S.J., KHAN, S., CLARE, S., WUNDERINK, R.G., LIU, H., WALUNAS, T., COOPER, L., FENG, Y., WEHBE, F., FANG, D., LIEBOVITZ, D.M., MARKL, M., MICHELSON, K.N., MCCOLLEY, S.A., GREEN, M., STARREN, J., ACKERMANN, R.T., D'AQUILA, R.,T., ADAMS, J., LLOYD-JONES, D., CHISHOLM, R.L. and KHO, A., 2024. Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system. Learning Health Systems, 8(3),.
MENG-LEONG HOW and SIN-MEI CHEAH, 2024. Forging the Future: Strategic Approaches to Quantum AI Integration for Industry Transformation. Ai, 5(1), pp. 290.
NAIR, M., SVEDBERG, P., LARSSON, I. and NYGREN, J.M., 2024. A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design. PLoS One, 19(8),.
ONYEJEKWE, E.R., PHD., SHERIFI, DASANTILA, PHD,M.B.A., R.H.I.A. and CHING, HUNG,PHD., D.A.B.R., 2024. Perspectives on Big Data and Big Data Analytics in Healthcare. Perspectives in Health Information Management, 21(1), pp. 1-19.
PDF, 2024. Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies. International Journal of Advanced Computer Science and Applications, 15(3),.
SHIVA, M.V. and FOROUZANFAR, M., 2024. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering, 11(4), pp. 337.
WILLIAMSON, S.M. and PRYBUTOK, V., 2024. Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare. Applied Sciences, 14(2), pp. 675.
YAZDI, M., ZAREI, E., ADUMENE, S. and BEHESHTI, A., 2024. Navigating the Power of Artificial Intelligence in Risk Management: A Comparative Analysis. Safety, 10(2), pp. 42.
ZAMANI, E.D., SMYTH, C., GUPTA, S. and DENNEHY, D., 2023. Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Annals of Operations Research, 327(2), pp. 605-632.
ZAVALETA-MONESTEL ESTEBAN, QUESADA-VILLASEÑOR, R., ARGUEDAS-CHACÓN SEBASTIÁN, GARCÍA-MONTERO, J., BARRANTES-LÓPEZ MONSERRAT, SALAS-SEGURA, J., ANCHÍA-ALFARO ADRIANA, NIETO-BERNAL, D. and DIAZ-JUAN, D., 2024. Revolutionizing Healthcare: Qure.AI's Innovations in Medical Diagnosis and Treatment. Cureus, 16(6),.
ZHU, Y., SALOWE, R., CHOW, C., LI, S., BASTANI, O. and JOAN M O’BRIEN, 2024. Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection. Bioengineering, 11(2), pp. 122.”
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