An Explainable Ensemble Framework for Alzheimer's Disease Prediction Using Structured Clinical and Cognitive Data

• URL: http://arxiv.org/abs/2603.04449v1
• Date: Thu, 26 Feb 2026 19:12:35 GMT
• Title: An Explainable Ensemble Framework for Alzheimer's Disease Prediction Using Structured Clinical and Cognitive Data
• Authors: Nishan Mitra,
• Abstract summary: This research introduces an explainable ensemble learning Framework designed to classify individuals as Alzheimer's or Non-Alzheimer's.<n>The framework incorporates rigorous preprocessing, advanced feature engineering, SMOTE-Tomek hybrid class balancing, and optimized modeling.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early and accurate detection of Alzheimer's disease (AD) remains a major challenge in medical diagnosis due to its subtle onset and progressive nature. This research introduces an explainable ensemble learning Framework designed to classify individuals as Alzheimer's or Non-Alzheimer's using structured clinical, lifestyle, metabolic, and lifestyle features. The workflow incorporates rigorous preprocessing, advanced feature engineering, SMOTE-Tomek hybrid class balancing, and optimized modeling using five ensemble algorithms-Random Forest, XGBoost, LightGBM, CatBoost, and Extra Trees-alongside a deep artificial neural network. Model selection was performed using stratified validation to prevent leakage, and the best-performing model was evaluated on a fully unseen test set. Ensemble methods achieved superior performance over deep learning, with XGBoost, Random Forest, and Soft Voting showing the strongest accuracy, sensitivity, and F1-score profiles. Explainability techniques, including SHAP and feature importance analysis, highlighted MMSE, Functional Assessment Age, and several engineered interaction features as the most influential determinants. The results demonstrate that the proposed framework provides a reliable and transparent approach to Alzheimer's disease prediction, offering strong potential for clinical decision support applications.

Related papers

• An Adaptive Machine Learning Triage Framework for Predicting Alzheimer's Disease Progression [12.418201300163545]
  Accurate predictions of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) can enable effective personalized therapy.<n>We design a two-stage machine learning framework that selectively obtains advanced, costly features based on their predicted "value of information"<n>Our framework reduces the need for advanced testing by 20% while achieving a test AUROC of 0.929, comparable to the model that uses both basic and advanced features.
  arXiv  Detail & Related papers  (2025-11-10T03:57:56Z)
• PSO-XAI: A PSO-Enhanced Explainable AI Framework for Reliable Breast Cancer Detection [2.5631347250059577]
  This study proposes an integrated framework that incorporates customized Particle Swarm Optimization (PSO) for feature selection.<n>The proposed approach achieved a superior score of 99.1% across all performance metrics, including accuracy and precision.<n>Results highlight the potential of combining swarm intelligence with explainable ML for robust, trustworthy, and clinically meaningful breast cancer diagnosis.
  arXiv  Detail & Related papers  (2025-10-23T14:42:50Z)
• Deep Learning Approaches with Explainable AI for Differentiating Alzheimer Disease and Mild Cognitive Impairment [0.0]
  Early and accurate diagnosis of Alzheimer Disease is critical for effective clinical intervention.<n>We propose a hybrid deep learning ensemble framework for Alzheimer Disease classification using structural magnetic resonance imaging.
  arXiv  Detail & Related papers  (2025-09-27T16:17:14Z)
• KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs [39.47350988195002]
  Large language models (LLMs) have shown promise in leveraging language abilities and biomedical knowledge for diagnosis prediction.<n>We propose KERAP, a knowledge graph (KG)-enhanced reasoning approach that improves LLM-based diagnosis prediction through a multi-agent architecture.<n>Our framework consists of a linkage agent for mapping, a retrieval agent for structured knowledge extraction, and a prediction agent that iteratively refines diagnosis predictions.
  arXiv  Detail & Related papers  (2025-07-03T16:35:11Z)
• An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm [0.0]
  This study proposes a novel deep learning-based framework for the automated detection of monkeypox from skin lesion images.<n>We utilize the newly developed Monkeypox Skin Lesion dataset (MSLD), which includes images of monkeypox, chickenpox, and measles, to train and evaluate our models.<n>Our results demonstrate that the proposed AVOA-NGBoost model achieves state-of-the-art performance, with an accuracy of 97.53%, F1-score of 97.72% and an AUC of 97.47%.
  arXiv  Detail & Related papers  (2025-04-24T13:32:11Z)
• CDS: Knowledge Component-Driven Data Synthesis Guided by Cognitive Diagnosis Theory [39.579188324839386]
  Large Language Models (LLMs) have achieved significant advancements, but the increasing complexity of tasks and higher performance demands highlight the need for continuous improvement.<n>Some approaches utilize synthetic data generated by advanced LLMs based on evaluation results to train models.<n>In this paper, we introduce the Cognitive Diagnostic Synthesis (CDS) method, which incorporates a diagnostic process inspired by Cognitive Diagnosis Theory (CDT) to refine evaluation results and characterize model profiles at the knowledge component level.
  arXiv  Detail & Related papers  (2025-01-13T20:13:59Z)
• MRI Patterns of the Hippocampus and Amygdala for Predicting Stages of Alzheimer's Progression: A Minimal Feature Machine Learning Framework [0.0]
  This study proposes a minimal-feature machine learning framework that leverages structural MRI data, focusing on the hippocampus and amygdala as regions of interest.<n>The framework addresses the curse of dimensionality through feature selection, utilizes region-specific voxel information, and implements innovative data organization to enhance classification performance by reducing noise.
  arXiv  Detail & Related papers  (2025-01-10T10:47:00Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection [69.53626024091076]
  Alzheimer's disease (AD) is particularly prominent in older adults. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
  arXiv  Detail & Related papers  (2023-03-14T16:03:28Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity [39.57255380551913]
  We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect Alzheimer's dementia (AD) and its severity. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression.
  arXiv  Detail & Related papers  (2020-08-30T21:47:26Z)
• Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment [38.936005220639316]
  A novel tensorizing GAN with high-order pooling is proposed to assess Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) To the best of our knowledge, the proposed-train, High-pooling and Semi-supervised learning based GAN (THS-GAN) is the first work to deal with classification on MRI images for AD diagnosis.
  arXiv  Detail & Related papers  (2020-08-03T10:04:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.