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Alzheimer's disease (AD) is a prominent nervous ailment. Even though its symptoms are mild at first, they get worse with time. To foretell the onset of AD utilizing psychological criteria such as age, gender, memory, Clinical Dementia Rating (CDR), and Mini-Mental State Examination (MMSE) scores, this study uses machine learning algorithms (MLAs): Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Support vector machine (SVM), and K closest neighbor (KNN) to identify AD in an Alzheimer's clinical dataset from the Kaggle Repository. We use five performance metrics for a comparative study: Accuracy, Precision, Recall, F1score, and AUC.

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Diagnosis of Alzheimer Disease Using Machine Learning Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Tanya Kumari,
  • Ritika Kumari,
  • Poonam Bansal

摘要

Alzheimer's disease (AD) is a prominent nervous ailment. Even though its symptoms are mild at first, they get worse with time. To foretell the onset of AD utilizing psychological criteria such as age, gender, memory, Clinical Dementia Rating (CDR), and Mini-Mental State Examination (MMSE) scores, this study uses machine learning algorithms (MLAs): Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Support vector machine (SVM), and K closest neighbor (KNN) to identify AD in an Alzheimer's clinical dataset from the Kaggle Repository. We use five performance metrics for a comparative study: Accuracy, Precision, Recall, F1score, and AUC.