Analysis of Clinical, Genetic, and Demographic Data for Prediction of Alzheimer's Disease with Machine Learning

Aug 4, 2026

Ageing populations and the rising prevalence of Alzheimer’s disease (AD) make early and accurate diagnosis essential for effective management and treatment. This study applies exploratory data analysis (EDA) to examine relationships among multiple risk factors and disease progression, while assessing how integrating clinical parameters, genetic markers, and demographic and lifestyle information can strengthen predictive modelling. Comparative experiments across machine learning algorithms evaluate performance gains driven by data quality, diversity, and multidimensional feature combination, supporting earlier detection in clinical decision-making.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Introduction

The importance of early detection of AD

AI vs. Traditional Methods in AD Diagnostics

Objectives of the paper

Methodology

Descriptive statistics across groups

Comparative analysis of genetic, lifestyle, and clinical parameters

Correlation analysis within IG1 group

Predictive modelling using machine learning algorithms

2024 10th International Conference on Control, Decision and Information Technologies (CoDIT) | 979-8-3503-7397-4/24/$31.00 ©2024 IEEE | DOI: 10. 1 109/CoDIT62066 .2024. 10708434 Analysis of Clinical, Genetic, and Demographic Data for Prediction of Alzheimer's Disease with Machine Learning Anita Petreska, Sasho Nikolovski, Gabriela Novotni, Blagoj Ristevski IEEE Senior Member Abstract— In the context of the ageing of the global population and the increasing prevalence of Alzheimer's disease (AD), early and accurate diagnosis is crucial for effective management and treatment. Using Exploratory Data Analysis (EDA) we dissect the complex relationships between various risk factors and disease progression, establishing a basis for our predictive modelling. Uncovering critical insights, and emphasizing the importance of adopting a multidimensional approach to analyze diverse datasets effectively, our study highlights the critical role of data quality and diversity in improving model performance. The fundamental aspect of our analysis focuses on the predictive power of combining different data types, which traditionally include clinical parameters, genetic markers, and demographic and lifestyle data. The research highlights the application of machine learning (ML) techniques for early detection and predictive analysis of Alzheimer's disease, demonstrating the enormous potential of artificial inelegance in transforming healthcare diagnostics. The study conducted a comparative analysis of various ML algorithms and evaluated their efficiency in disease detection. This research contributes to the academic discourse on the diagnosis of Alzheimer's disease and provides practical insights for the application of artificial intelligence and machine learning in clinical practice. Keywords: Alzheimer's Disease, Machine Learning, Predictive Analysis, Exploratory Data Analysis. I. INTRODUCTION Using traditional diagnostic methods due to the wide time frames often leads to AD patients receiving the diagnosis at a late stage of the disease. A. The importance of early detection of AD AD leads to significant disability and dependency in the elderly, and the challenges it poses apply not only to people with AD but also to their families. In the modern world, although there is a solid level of awareness about this disease, the lack of widespread awareness and understanding of AD can still be observed, which leads to stigmatisation and obstacles in the early diagnosis and appropriate care, and the consequences are reflected through the physical, Anita Petreska is with the Faculty of Information and Communication Technologies, University “St. Kliment Ohridski” - Bitola, North Macedonia, [e-mail: petreska.anita@uklo.edu.mk](e-mail: petreska.anita@uklo.edu.mk) Sasho Nikolovski is with the Faculty of Information and Communication Technologies, University “St. Kliment Ohridski” - Bitola, North Macedonia, e-mail: [sasnik@gmail.com](sasnik@gmail.com) Gabriela Novotni is with the University Clinic for Neurology, Faculty of Medicine, Ss Cyril and Methodius University of Skopje, Skopje, North Macedonia Blagoj Ristevski is with the Faculty of Information and Communication Technologies, University “St. Kliment Ohridski” -Bitola, Macedonia [e-mail: blagoj.ristevski@uklo.edu.mk](e-mail: blagoj.ristevski@uklo.edu.mk) psychological, social and economic dimensions, affecting caregivers, families and society at large [1] . B. AI vs. Traditional Methods in AD Diagnostics Diagnosing AD using traditional diagnostics is an expensive, error-prone method due to the influence of factors such as human fatigue, cognitive biases [18], as well as systematic errors. On the contrary, diagnostic systems based on artificial intelligence [1][2][9][12] represent promising solutions, more reliable and less prone to errors, of course, provided that they are correctly set up and dimensioned according to the requirements and the data material that is the

FAQs

Why is early detection of Alzheimer’s disease important in the paper?

Traditional diagnostic approaches often diagnose AD late due to long time frames. Early detection reduces disability and dependency in older adults and improves pathways for appropriate care.

What data types are combined to improve AD prediction?

The study integrates clinical parameters, genetic markers, and demographic and lifestyle data, using EDA to understand relationships and guide predictive modelling.

How does the research compare machine learning approaches for AD detection?

It performs a comparative analysis of multiple machine learning algorithms and evaluates their efficiency for disease detection, focusing on improved performance supported by data quality and diversity.

Aug 4, 2026

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