Prediction of Chronic Disease Using Explainable Hybrid Machine Learning

October 5, 2026 updated by: Gülşen Taşkın, Afyonkarahisar Health Sciences University

Physical Activity and Lifestyle-Based Prediction of Chronic Disease Using Explainable Hybrid Machine Learning

In this study, a 28-item questionnaire developed by the researchers (Aysun Atacan and Gülşen Taşkın) will be used as the data collection tool. Since the intellectual property rights and developer identity belong to the researchers, there are no copyright or usage restrictions. Containing both qualitative and quantitative evaluation questions, the questionnaire consists of 5 main sections: personal information, daily life assessment, walking level assessment, household chores assessment, and work/workplace assessment. Eight of the questions cover sociodemographic information, while 20 focus on determining and evaluating physical activity levels in daily life, walking, home, work, and transportation. Participants are asked to answer the questions by considering their lifestyle over the past month. Based on these data-recorded according to how many days per week and how many minutes per day the activities are performed-calculations will be made by multiplying the MET value, frequency (days/week), and duration (minutes/day) to obtain the "MET-min/week" score. Participants will be categorized into three groups based on their total weekly MET expenditures: <600 MET-min/week as inactive, 600-3000 MET-min/week as minimally active, and >3000 MET-min/week as health-enhancing physically active (sufficiently active); and into groups based on their average daily step counts: <5000 as sedentary/inactive, 5001-7499 as low active, 7500-9999 as somewhat active, 10000-12499 as active, and >12500 as highly active. Chronic disease risk predictions will then be evaluated via machine learning based on these physical activity levels and step counts.

The primary aim of this research is to quantitatively demonstrate the impact of physical activity levels and lifestyle habits on chronic disease risk factors using machine learning (ML) methods. The models to be developed aim not only for high predictive performance but also for the clinical interpretation of which physical activity parameters are more decisive on disease risk, utilizing explainable artificial intelligence methods such as SHapley Additive exPlanations (SHAP). Consequently, the goal is to establish a decision support mechanism for the early detection of individuals at risk and to place personalized exercise prescriptions on a scientific foundation.

Study Overview

Status

Active, not recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

268

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

Turkey (Türkiye)
- Merkez
  - Afyonkarahisar, Merkez, Turkey (Türkiye)
    - Afyonkarahisar Health Sciences University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 64 years (Adult)

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

18-64 aged

Description

Inclusion Criteria:

Being aged between 18 and 64
Participating in the study voluntarily

Exclusion Criteria:

Not being aged between 18 and 64 Not agreeing to participate in the study

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Observational Models: OTHER
Time Perspectives: CROSS_SECTIONAL

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
hypothesis Time Frame: Baseline	Estimation of the presence of chronic disease within an 85-95% confidence interval, based on physical activity and demographic data.	Baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
hypothesis Time Frame: Baseline	Identification of the most effective algorithm by comparing the sensitivity and specificity values of the developed models.	Baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Afyonkarahisar Health Sciences University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 30, 2026

Primary Completion (Estimated)

November 30, 2026

Study Completion (Estimated)

December 31, 2026

Study Registration Dates

First Submitted

September 22, 2026

First Submitted That Met QC Criteria