Leveraging AI for Predictive Diagnosis and Treatment in Telemedicine: A Case Study in Illness Prediction Using Machine Learning Models

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dc.contributor.authorAbia V.M.
dc.contributor.authorOkoro A.E.
dc.contributor.authorUbom E.A.
dc.date.accessioned2024-11-25T16:26:07Z
dc.date.available2024-11-25T16:26:07Z
dc.date.issued2024-11-25
dc.descriptionScholarly article
dc.description.abstractThe addition of artificial intelligence into telemedicine has improved healthcare services delivery, especially in predictive diagnosis and treatment in recent years. This study involves the application of four machine learning models for illness prediction based on a comprehensive dataset sourced from Kaggle, an online platform for machine learning practices. Aimed at developing robust predictive models through effective preprocessing and analysis of symptom features, Support Vector Machines (SVM), Naive Bayes, Random Forest, and Logistic Regression algorithms were employed. From the study's findings, SVM and Logistic Regression outperformed the other disease categorization techniques. The Random Forest algorithm faced some issues as it misclassified a large proportion of fungal infection cases. Raising questions regarding its dependability in clinical applications. The results were visualized using heatmaps, allowing the identification of misclassifications and providing insights into the strengths and weaknesses of the algorithms. This research shows the potential of artificial intelligence predictive models in telemedicine to improve diagnostic accuracy while showing the importance of continuous model refinement and evaluation. The findings support the integration of machine learning in clinical practice, showing the need for ongoing research to optimize these technologies for effective healthcare delivery in an increasingly data-driven world.
dc.identifier.citationAbia V.M., Okoro A.E. & Ubom E.A. (2024). “Leveraging AI for Predictive Diagnosis and Treatment in Telemedicine: A Case Study in Illness Prediction Using Machine Learning Models ". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2024), pp. 57-64, MIRG
dc.identifier.isbn978-978-771-680-9
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/13048
dc.language.isoen
dc.publisherMIRG
dc.relation.ispartofseriesMIRG-ICAIR 2024
dc.titleLeveraging AI for Predictive Diagnosis and Treatment in Telemedicine: A Case Study in Illness Prediction Using Machine Learning Models
dc.typeArticle
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