Download PDFOpen PDF in browserMachine Learning in Alzheimer's Prediction: A Comprehensive Study and Evaluation9 pages•Published: August 6, 2024AbstractAlzheimer's disease, a neurodegenerative disorder with profound societal impact, necessitates robust predictive models for early detection. This research delves into the realm of Alzheimer's prediction, concentrating on the efficacy of various machine learning algorithms. Gaining knowledge from previously published works in the Scopus database, we carried out a thorough review to identify recurring machine learning concepts. Our study synthesized information from diverse sources, revealing seven frequently employed machine learning algorithms. Through meticulous analysis of these algorithms, we discovered that Support Vector Machines (SVM) emerged as the most effective predictor, exhibiting superior performance in comparison to other models. The evaluation process included considerations of accuracy, sensitivity, and specificity, with SVM consistently outperforming its counterparts. Additionally, Random Forest emerged as a noteworthy alternative, showcasing commendable predictive capabilities. This study not only demonstrates the significance of machine learning in Alzheimer's prediction but also offers perceptive data for choosing the most efficient algorithmic approach. Our findings underscore the potential of SVM and Random Forest in enhancing diagnostic accuracy, laying the foundation for future advancements in early Alzheimer's detection and intervention. As the prevalence of Alzheimer's continues to rise, our work seeks to inform and guide scholars and professionals involved in the creation of efficient and reliable predictive models for improved patient outcomes.Keyphrases: alzheimer s disease, machine learning, prediction models In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 94-102.
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