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Alzheimer’s disease is a progressive brain condition defined by the loss of cognitive functions. Affected individuals exhibit poor motor coordination, which compromises their ability on written expression. The aim is to implement computational learning models as reliable complements to common diagnostic techniques, enhancing the specificity of Alzheimer’s diagnosis. In this study, three learning models were implemented: KNN, Naive Bayes and ANN, yielding F1-Scores of 0.7, 0.88 and 0.96 respectively. These learning models are crucial as non-invasive diagnostic techniques, enabling early detection of neurodegenerative diseases.

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ML Design in Handwriting Analysis for Classification of Alzheimer’s Disease

  • 会议论文

  • 发表日期：

    2025年

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  • Fabián Cienfuegos Caraveo,
  • Karely A. Álvarez Cruz,
  • Marianna Pacheco Quintana,
  • Elma N. Romero Ramos,
  • Celia María Quiñonez Flores,
  • Carlos Eduardo Cañedo Figueroa

摘要

Alzheimer’s disease is a progressive brain condition defined by the loss of cognitive functions. Affected individuals exhibit poor motor coordination, which compromises their ability on written expression. The aim is to implement computational learning models as reliable complements to common diagnostic techniques, enhancing the specificity of Alzheimer’s diagnosis. In this study, three learning models were implemented: KNN, Naive Bayes and ANN, yielding F1-Scores of 0.7, 0.88 and 0.96 respectively. These learning models are crucial as non-invasive diagnostic techniques, enabling early detection of neurodegenerative diseases.