A Review of Recent Advances in Alzheimer's Disease Machine Learning Algorithms for Early Mild Cognitive Impairment Prediction

Aug 3, 2026

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Research into early Alzheimer’s disease (AD) focuses on accurately forecasting clinical progression from normal cognition to mild cognitive impairment (MCI), from MCI to dementia, and also distinguishing non-progression pathways. Existing diagnostic and prediction approaches may show limited accuracy for symptomatic progression, motivating earlier identification. This overview consolidates studies since 2016 by comparing participant cohorts, data modalities, feature extraction methods, follow-up lengths, expected progression time, and machine learning models. It summarizes earlier work for novice researchers and supports objective literature evaluation aligned with core machine learning concepts.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Alzheimer's disease background and progression

Diagnostic technologies and early detection rationale

Machine learning and interpretability considerations

A Review of Recent Advances in Alzheimer's Disease Machine Learning Algorithms for Early Mild Cognitive Impairment Prediction SEEJPH Volume XXVI, 2025, ISSN: 2197-5248; Posted:04-01-25 A Review of Recent Advances in Alzheimer's Disease Machine Learning Algorithms for Early Mild Cognitive Impairment Prediction Saranya Rathinam1*, Krishnan Nallaperumal2, Kalidass Subramaniam3 1*, 2Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627012, Tamil Nadu, India, [saranyadass88@gmail.com](saranyadass88@gmail.com), [2](2krishnan17563@gmail.com)[krishnan17563@gmail.com](2krishnan17563@gmail.com), 3Department of Animal Science, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627012, Tamil Nadu, India, [kallidass@gmail.com](kallidass@gmail.com) KEYWORDS ABSTRACT: Alzheimer's Research into early Alzheimer's disease (AD) is the main focus of clinical investigations. Clinical progression predictions from normal to moderate cognitive impairment (MCI), disease, moderate MCI to dementia, AD, or non-progression are not very accurate. Medication utilization is cognitive decreased and trial efficiency is increased with accurate symptomatic progressor impairment, identification. Preparing for Alzheimer's therapy would thus be easier with an early machine learning, diagnosis. As a result, the disease could develop more slowly. Alzheimer's may be Alzheimer's therapy recognized using machine learning algorithms. The performance categorization of patients with Alzheimer's disease may be improved using advanced machine learning. As a result, this research builds upon previous diagnostic studies of Alzheimer's disease conducted since 2016. Participant nation, data modalities and characteristics, feature extraction techniques, number of follow-up data points, anticipated time from mild cognitive impairment to Alzheimer's disease, and machine learning models are all taken into account in this overview of studies on Alzheimer's detection. The characteristics and machine learning models used in earlier Alzheimer's research may be explained to novice researchers by this review. Because it is structured to adhere to the many elements of the Machine Learning technique, this study aids researchers in objectively assessing the literature on Alzheimer's detection. learning models used in earlier Alzheimer's research may be explained to novice researchers by this review. Because it is structured to adhere to the many elements of the Machine Learning technique, this study aids researchers in objectively assessing the literature on Alzheimer's detection. 1. Introduction The gradual breakdown of brain cell protein components, leading to the accumulation of plaquesand tangles, is the hallmark of Alzheimer's disease (AD), a neurodegenerative disorder [1] . Cognitive function deteriorates dramatically because these abnormal proteins block their components from interacting with one another. There is a 10% probability that mild cognitive impairment (MCI) may progress to Alzheimer's disease (AD) [2, 3] . Mild cognitive impairment (MCI) is a transitional condition between CN and dementia. With an estimated 55 million cases globally, Alzheimer's disease is the sixth biggest killer [4] . According to the most current worldwide Alzheimer's Report, this data was gathered. It might take a long time to diagnose Alzheimer's disease. Biomarkers for Alzheimer's disease may now be efficiently collected with the use of diagnostic technologies including positron emission tomography (PET) scans, computed tomography (CT), and magnetic resonance imaging (MRI) [5] . Artificial intelligence (AI) combined with biomarker data could pave the way for earlier disease detection. Anomaly detection, signal analysis, assessment and classification of neurodevelopmental disorders (specifically autism), detection and management of neurological disorders, monitoring and care for the elderly,

FAQs

What progression stages does the review focus on for Alzheimer’s disease prediction?

The review focuses on clinical progression from normal cognition to mild cognitive impairment (MCI), from MCI to dementia, and on distinguishing non-progression pathways.

Why is early diagnosis of Alzheimer’s disease difficult and what helps improve it?

The document notes that diagnosis can take a long time, while biomarker collection supported by tools such as PET, CT, and MRI enables more efficient early detection. It also highlights that combining AI with biomarker data can support earlier detection.

How does the review address the interpretability problem in machine learning models?

It states that limited transparency and interpretability hinder mainstream use and emphasizes explainable AI (XAI). Examples mentioned include saliency maps, feature significance analysis, SHAP, and LIME.

Aug 3, 2026

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