Predicting Alzheimer’s Disease with Interpretable Machine Learning

Jia, Maoni; Wu, Yafei; Xiang, Chaoyi; Fang, Ya

doi:10.1159/000531819

Dement Geriatr Cogn Disord

2023

DOI: 10.1159/000531819

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Predicting Alzheimer’s Disease with Interpretable Machine Learning

Maoni Jia¹,

Yafei Wu²,

Chaoyi Xiang³

et al.

Abstract: Introduction: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We included 1219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above thre… Show more

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Cited by 2 publications

References 31 publications

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Advancing Alzheimer’s Disease Risk Prediction: Development and Validation of a Machine Learning-Based Preclinical Screening Model

wang,

Chen,

Shi

et al. 2023

Preprint

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Background: Alzheimer’s disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Most existing Alzheimer’s disease risk predic- tion tools have high accuracy, but the complexity and limited accessibility of current AD risk prediction tools hinder their practical use. Objective: Our goal was to leverage machine learning techniques to develop a prediction model that is not only highly efficient but also cost-effective. METHODS: Utilizing data from 2,968 individuals sourced from the National Alzheimer’s Coor- dinating Center, and we constructed models, including gradient-enhanced machines and random forests, as well as commonly used logistic regression models. For modeling purposes, we employed two popular machine learning algorithms, Random Forest and XGBoost, along with traditional logistic regression methods. The models’ performance was evaluated based on five key criteria: the Brier score, accuracy (ACC), specificity (SPE), sensitivity (SEN), and area under the receiver operating characteristic curve (AUC). RESULTS: The average age of the 2968 participants was 71.1 years, with a standard deviation of 6.8 years, and 60.3% were female. The prevalence of AD was 23.15% (n= 687). The machine learning-based Boruta algorithm identified 16 significant predictors from 33 potential risk factors, with a minimum Root mean squared error (RMSE) of 0.27 when the top 5 variables were selected (education level, depression, rapid eye movement sleep disorder, age, anxiety).We used the SHAP feature in the Gradient Boosting Decision Tree Model importance to rank the top 20 significant predictors and selected the top 4 variables: edu- cation level, age, marital status, and depression to construct our model based on cross-validation results. Compared to the logistic regression model, the integrated algorithm XGBoost and the random forest model performed better. Notably, XGBoost outperformed other models, achievingan AUC score of 0.78, ACC score of 0.691, SPE score of 0.677, SEN score of 0.739, PRE score of 0.403, and Brier score of 0.140. CONCLUSION: Individual characteristics and psychological status are more critical than past history. Machine-learning-based AD risk assessment tools for older adults can be easily accessed and show some accurate discrimination, which may be useful in guiding preclinical screening for AD in the elderly population.

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Advancing Alzheimer’s Disease Risk Prediction: Development and Validation of a Machine Learning-Based Preclinical Screening Model

wang,

Chen,

Shi

et al. 2023

Preprint

View full text Add to dashboard Cite

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Advancing Alzheimer's Disease Risk Prediction: Development and Validation of a Machine Learning-Based Preclinical Screening Model (Preprint)

Cao,

wang,

shi

et al. 2023

Preprint

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BACKGROUND Alzheimer's disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Most existing Alzheimer's disease risk prediction tools have high accuracy, but the complexity and limited accessibility of current AD risk prediction tools hinder their practical use. OBJECTIVE Our goal was to leverage machine learning techniques to develop a prediction model that is not only highly efficient but also cost-effective. METHODS Utilizing data from 2,968 individuals sourced from the National Alzheimer's Coordinating Center, and we constructed models, including gradient-enhanced machines and random forests, as well as commonly used logistic regression models. For modeling purposes, we employed two popular machine learning algorithms, Random Forest and XGBoost, along with traditional logistic regression methods. The models' performance was evaluated based on five key criteria: the Brier score, accuracy (ACC), specificity (SPE), sensitivity (SEN), and area under the receiver operating characteristic curve (AUC). RESULTS The average age of the 2968 participants was 71.1 years, with a standard deviation of 6.8 years, and 60.3% were female. The prevalence of AD was 23.15% (n= 687). The machine learning-based Boruta algorithm identified 16 significant predictors from 33 potential risk factors, with a minimum Root mean squared error (RMSE) of 0.27 when the top 5 variables were selected (education level, depression, rapid eye movement sleep disorder, age, anxiety).We used the SHAP feature in the Gradient Boosting Decision Tree Model importance to rank the top 20 significant predictors and selected the top 4 variables: education level, age, marital status, and depression to construct our model based on cross-validation results. Compared to the logistic regression model, the integrated algorithm XGBoost and the random forest model performed better. Notably, XGBoost outperformed other models, achieving an AUC score of 0.78, ACC score of 0.691, SPE score of 0.677, SEN score of 0.739, PRE score of 0.403, and Brier score of 0.140. CONCLUSIONS Individual characteristics and psychological status are more critical than past history. Machine-learning-based AD risk assessment tools for older adults can be easily accessed and show some accurate discrimination, which may be useful in guiding preclinical screening for AD in the elderly population.

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Predicting Alzheimer’s Disease with Interpretable Machine Learning

Cited by 2 publications

References 31 publications

Advancing Alzheimer’s Disease Risk Prediction: Development and Validation of a Machine Learning-Based Preclinical Screening Model

Advancing Alzheimer’s Disease Risk Prediction: Development and Validation of a Machine Learning-Based Preclinical Screening Model

Advancing Alzheimer's Disease Risk Prediction: Development and Validation of a Machine Learning-Based Preclinical Screening Model (Preprint)

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