Prediction of Alzheimer's Dementia Using Soft Voting Ensemble Learning with Machine Learning

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's dementia (AD) is a degenerative brain disease characterized by a decline in cognitive function and memory. Predicting AD is crucial for preventing the disease from becoming more severe. Machine learning algorithms can aid in the early prediction of AD. The aim of this study is to develop a predictive model with improved accuracy using ensemble learning methods and machine learning algorithms. The experiment used the Oasis Longitudinal dataset from Oasis Brains, which includes details of patients with and without AD. This study proposed a binary classification using an ensemble learning classifier with the soft voting technique, incorporating four ensemble learning algorithms: Extreme Gradient Boosting (XGB), Random Forest (RF), AdaBoost (ADC), and Gradient Boosting (GBC). The proposed method is evaluated based on accuracy, precision, recall, and F1-score of the classification. The experimental results show that the combination of the Random Over Sampling (ROS) resampling method and grid search hyperparameter tuning for the Gradient Boosting algorithm achieved the best results, with an accuracy of 97%.

Key findings

• The experimental results show that the combination of the Random Over Sampling (ROS) resampling method and grid search hyperparameter tuning for the Gradient Boosting algorithm achieved the best results, with an accuracy of 97%.

Why it matters

Using the Oasis Longitudinal dataset, an ensemble machine learning method with soft voting and Gradient Boosting achieved 97% accuracy in predicting Alzheimer's dementia. This is a prediction model only and does not show how to treat, cure, or prevent the disease.

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Study details

• Study designlongitudinal study
• Evidence strengthModerate

Read the original

• DOI: 10.71129/ijaci.v1.i1.pp48-55
• Original source →
• Free full text: IJACI International Journal of Advanced Computing and Informatics →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{rudini2025-e019ec09,
  author  = {Rudini, Edwin and Ernawan, Ferda},
  title   = {{Prediction of Alzheimer's Dementia Using Soft Voting Ensemble Learning with Machine Learning}},
  journal = {IJACI International Journal of Advanced Computing and Informatics},
  year    = {2025},
  doi     = {10.71129/ijaci.v1.i1.pp48-55},
  url     = {https://doi.org/10.71129/ijaci.v1.i1.pp48-55}
}

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