Prediction of Chronic Disease Using Explainable Hybrid Machine Learning

NCT07845084 | Active Not Recruiting

• 1 other identifier: 2026/495
• Study Type: observational
• Target: 268
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Physical Activities; Machine Learning

Keywords

Chronic Disease Prediction; Explainable Artificial Intelligence; Hybrid Machine Learning; Lifestyle Factors; Physical Activity; Risk Stratification

Outcome Measures

Primary Outcomes (1)

• hypothesis

  Estimation of the presence of chronic disease within an 85-95% confidence interval, based on physical activity and demographic data.

  Baseline

Secondary Outcomes (1)

• hypothesis

  Baseline

Eligibility Criteria

• Age: 18 Years - 64 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• Sampling Method: Probability Sample

Study Population

18-64 aged

You may qualify if:

• Being aged between 18 and 64
• Participating in the study voluntarily

You may not qualify if:

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

Sponsors & Collaborators

• Afyonkarahisar Health Sciences University: lead

Study Sites (1)

Afyonkarahisar Health Sciences University

Afyonkarahisar, Merkez, Turkey (Türkiye)

Location

MeSH Terms

Conditions

Motor Activity

Condition Hierarchy (Ancestors)

Behavior

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Asist. Prof

Study Record Dates

• First Submitted: September 22, 2026
• First Posted: September 28, 2026
• Study Start: August 30, 2026
• Primary Completion (Estimated): November 30, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: September 28, 2026

Record last verified: 2026-09

Locations

TU (1)