Evaluation of machine learning models for the prediction of Alzheimer's: In search of the best performance
Alzheimer's is a progressive and degenerative disease affecting millions worldwide, incapacitating them physically and cognitively. This study aims to perform a comparative analysis of Machine Learning models to determine the model with the best performance in predicting Alzheimer's diseas...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Brain, Behavior, & Immunity - Health |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666354625000158 |
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| author | Michael Cabanillas-Carbonell Joselyn Zapata-Paulini |
| author_facet | Michael Cabanillas-Carbonell Joselyn Zapata-Paulini |
| author_sort | Michael Cabanillas-Carbonell |
| collection | DOAJ |
| description | Alzheimer's is a progressive and degenerative disease affecting millions worldwide, incapacitating them physically and cognitively. This study aims to perform a comparative analysis of Machine Learning models to determine the model with the best performance in predicting Alzheimer's disease. The models used were Random Forest (RF), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K-nearest Neighbors (KNN), and Logistic Regression (LR). Two datasets called OASIS were used to train the models, the first one had a total of 436 records and 12 variables, while the second one stored 373 records and 15 variables. The article's content is divided into six main sections: introduction, literature review, methodological approach, results, discussions, and conclusions. After processing and pooling the datasets, RF, SVM, and LR proved the best predictors, achieving 96% accuracy, precision, sensitivity, and F1 score. This study highlights the efficacy of RF, SVM, and LR in predicting Alzheimer's disease, offering a significant advance toward understanding and management of this disease, which supports the relevance of implementing these models in future research and clinical applications. |
| format | Article |
| id | doaj-art-d7183c6d3c614264b30285eea90c3be0 |
| institution | Kabale University |
| issn | 2666-3546 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain, Behavior, & Immunity - Health |
| spelling | doaj-art-d7183c6d3c614264b30285eea90c3be02025-02-09T05:01:28ZengElsevierBrain, Behavior, & Immunity - Health2666-35462025-03-0144100957Evaluation of machine learning models for the prediction of Alzheimer's: In search of the best performanceMichael Cabanillas-Carbonell0Joselyn Zapata-Paulini1Faculty of Engineering, Universidad Privada del Norte, Lima, PeruGraduate School, Universidad Continental, Lima, Peru; Corresponding author.Alzheimer's is a progressive and degenerative disease affecting millions worldwide, incapacitating them physically and cognitively. This study aims to perform a comparative analysis of Machine Learning models to determine the model with the best performance in predicting Alzheimer's disease. The models used were Random Forest (RF), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K-nearest Neighbors (KNN), and Logistic Regression (LR). Two datasets called OASIS were used to train the models, the first one had a total of 436 records and 12 variables, while the second one stored 373 records and 15 variables. The article's content is divided into six main sections: introduction, literature review, methodological approach, results, discussions, and conclusions. After processing and pooling the datasets, RF, SVM, and LR proved the best predictors, achieving 96% accuracy, precision, sensitivity, and F1 score. This study highlights the efficacy of RF, SVM, and LR in predicting Alzheimer's disease, offering a significant advance toward understanding and management of this disease, which supports the relevance of implementing these models in future research and clinical applications.http://www.sciencedirect.com/science/article/pii/S2666354625000158AlzheimerPredictionMachine learningEvaluationModels |
| spellingShingle | Michael Cabanillas-Carbonell Joselyn Zapata-Paulini Evaluation of machine learning models for the prediction of Alzheimer's: In search of the best performance Brain, Behavior, & Immunity - Health Alzheimer Prediction Machine learning Evaluation Models |
| title | Evaluation of machine learning models for the prediction of Alzheimer's: In search of the best performance |
| title_full | Evaluation of machine learning models for the prediction of Alzheimer's: In search of the best performance |
| title_fullStr | Evaluation of machine learning models for the prediction of Alzheimer's: In search of the best performance |
| title_full_unstemmed | Evaluation of machine learning models for the prediction of Alzheimer's: In search of the best performance |
| title_short | Evaluation of machine learning models for the prediction of Alzheimer's: In search of the best performance |
| title_sort | evaluation of machine learning models for the prediction of alzheimer s in search of the best performance |
| topic | Alzheimer Prediction Machine learning Evaluation Models |
| url | http://www.sciencedirect.com/science/article/pii/S2666354625000158 |
| work_keys_str_mv | AT michaelcabanillascarbonell evaluationofmachinelearningmodelsforthepredictionofalzheimersinsearchofthebestperformance AT joselynzapatapaulini evaluationofmachinelearningmodelsforthepredictionofalzheimersinsearchofthebestperformance |