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This study employs a stacking ensemble model combining Logistic Regression, Extra Trees, Bagging KNN, and LightGBM, alongside a deep artificial neural network, to predict early stages of Alzheimer's disease using clinical data, neuropsychological test scores, and neuroimaging measures. By addressing challenges such as missing values and class imbalance through iterative imputation and Borderline SVM-SMOTE, the research identifies key biomarkers that facilitate early diagnosis. The comparative analysis of model performance reveals significant insights into the most effective classifiers for early Alzheimer's detection, emphasizing the importance of specific features in diagnosis.
Early detection of Alzheimer's is revolutionized by identifying critical biomarkers through advanced machine learning techniques, potentially transforming patient management.
Alzheimers disease (AD) is a brain disorder that develops slowly and mainly affects memory, thinking, language, and daily activities. It is one of the most common causes of dementia and creates many difficulties for patients as well as their families. In the early stage, the symptoms are often mild and may look like normal ageing. For this reason, many people are diagnosed late, when the disease has already progressed. At present, there is no complete cure for AD. Still, early detection can help doctors manage the condition better and take suitable steps at the right time. In this study, a machine learning model is proposed to detect the early stages of Alzheimers disease using clinical details, neuropsychological test scores, and neuroimaging-related measures. The data used in this work is collected from the Alzheimers Disease Neuroimaging Initiative (ADNI). As the dataset has missing values, iterative imputation is applied to fill them. The dataset also has class imbalance, which is handled using Borderline SVM-SMOTE. After that, feature selection is carried out using wrapper-based and embedded methods so that only important features are used for training. The selected features are divided into training and testing sets, and feature scaling is applied. A stacking ensemble model is developed using Logistic Regression, Extra Trees, Bagging KNN, and LightGBM as base classifiers. Along with this, an artificial neural network is also trained on the same dataset. The performance of these models is compared using precision, recall, F1-score, and AUC-ROC. This study aims to find the best classifier and also identify important biomarkers that may help in the early diagnosis of Alzheimers disease.
