Correlation vs causation in Alzheimer's disease: an interpretability-driven study
Understanding the distinction between causation and correlation is critical in Alzheimer's disease (AD) research, as it impacts diagnosis, treatment, and the identification of true disease drivers. This experiment investigates the relationships among clinical, cognitive, genetic, and biomarker features using a combination of correlation analysis, machine learning classification, and model interpretability techniques. Employing the XGBoost algorithm, we identified key features influencing AD classification, including cognitive scores and genetic risk factors. Correlation matrices revealed clusters of interrelated variables, while SHAP (SHapley Additive exPlanations) values provided detailed insights into feature contributions across disease stages. Our results highlight that strong correlations do not necessarily imply causation, emphasizing the need for careful interpretation of associative data. By integrating feature importance and interpretability with classical statistical analysis, this work lays groundwork for future causal inference studies aimed at uncovering true pathological mechanisms. Ultimately, distinguishing causal factors from correlated markers can lead to improved early diagnosis and targeted interventions for Alzheimer's disease.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal InferenceFeature ImportanceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Application of Time-Aware PC algorithm to compute Causal Functional Connectivity in Alzheimer's Disease from fMRI data
Functional Connectivity between brain regions is known to be altered in Alzheimer's disease, and promises to be a biomarker for early diagnosis of the disease. While several approaches for functional connectivity obtain …
Functional ConnectivityTime SeriesMulti-Task Learning with Feature-Similarity Laplacian Graphs for Predicting Alzheimer's Disease Progression
Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder in aging populations, posing a significant and escalating burden on global healthcare systems. While Multi-Tusk Learning (MTL) has emerged as a po…
Multi-Task LearningGene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across indiv…
Deep ClusteringUnlocking Neural Transparency: Jacobian Maps for Explainable AI in Alzheimer's Detection
Alzheimer's disease (AD) leads to progressive cognitive decline, making early detection crucial for effective intervention. While deep learning models have shown high accuracy in AD diagnosis, their lack of interpretabil…
Alzheimer's DetectionDiagnosticAn Adaptive Machine Learning Triage Framework for Predicting Alzheimer's Disease Progression
Accurate predictions of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) can enable effective personalized therapy. While cognitive tests and clinical data are routinely collected, they lack th…