Efficient Explainable Models for Alzheimer's Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning.

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

The trial behind it

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Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

1. Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026
   Pooled it
2. FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data.Diagnostics (Basel, Switzerland) · 2026
   Article
3. FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.Frontiers in neuroinformatics · 2026
   Article
4. Early Diagnosis of Alzheimer's Disease in Human Participants Using EEGConformer and Attention-Based LSTM During the Short Question Task.Diagnostics (Basel, Switzerland) · 2025
   Article

Corrections and comments

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Authors and funding

4 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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