用机器学习早期识别阿尔茨海默病，找出关键生物标志物。
Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
- 融合临床、认知测试和影像数据，用集成学习模型提升诊断精度。
- 在ADNI数据集上达到94.3%准确率，优于单一模型。
- 揭示多个重要生物标志物，助力早筛与机制研究。
阿尔茨海默病（AD）是一种缓慢发展的脑部疾病，主要影响记忆、思维、语言和日常活动，是痴呆最常见的原因。早期症状常轻微，易被误认为正常衰老，导致多数患者确诊时已进展。目前尚无根治方法，但早期发现可帮助医生更好地管理病情。本研究基于阿尔茨海默病神经影像计划（ADNI）数据集，利用临床信息、神经心理学测试分数及神经影像指标，构建机器学习模型以检测早期阶段。针对数据缺失，采用迭代插补法；针对类别不平衡，使用Borderline SVM-SMOTE处理。通过包装法与嵌入法进行特征选择，保留关键变量。训练集与测试集划分后进行特征缩放，构建包含逻辑回归、额外树、袋装KNN与LightGBM的堆叠集成模型，并对比人工神经网络性能。评估指标包括精确率、召回率、F1-score与AUC-ROC。结果表明该集成模型表现最优，同时识别出对早期诊断具有重要意义的生物标志物。
原文摘要 · Abstract (English)
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.
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