想用机器学习做早期疾病诊断？这篇论文用ADNI数据对比了集成模型和神经网络，还识别出了关键生物标志物，实操参考性强。
本研究提出基于临床数据、神经心理学测试和神经影像指标的机器学习模型，用于检测阿尔茨海默病早期阶段。数据来自ADNI（Alzheimer's Disease Neuroimaging Initiative），使用迭代插补处理缺失值，Borderline SVM-SMOTE处理类别不平衡。特征选择后，基于Logistic Regression、Extra Trees、Bagging KNN和LightGBM构建堆叠集成模型，并单独训练人工神经网络。模型性能通过精确率、召回率、F1分数和AUC-ROC对比，旨在找出最佳分类器和关键生物标志物。
Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
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.