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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Optimising hyperparameters with a tree structured Parzen estimator to improve diabetes prediction.

Raafat M Munshi¹, Lammar R Munshi², Hanen Himdi³

¹Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia. rmonshi@kau.edu.sa.

Scientific Reports

|October 10, 2025

Summary

Machine learning, using XGBoost, improves diabetes risk prediction from lab tests. This approach enhances accuracy in identifying high-risk patients for better health outcomes.

Keywords:

Diabetes Diagnostic strategies Laboratory parameters Machine learning Optuna Risk prediction XGBoost

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Area of Science:

Endocrinology and Metabolic Diseases
Medical Informatics
Computational Biology

Background:

Diabetes mellitus is a chronic condition impacting insulin production, necessitating early identification of high-risk individuals for effective management.
Traditional risk prediction models using clinical data have limitations in accuracy and broad applicability.
Machine learning (ML) offers potential for enhanced diagnostic strategies in diabetes risk stratification.

Purpose of the Study:

To develop and validate a machine learning-based diagnostic strategy for improved prediction of high-risk diabetes patients.
To leverage the XGBoost algorithm, optimized with Optuna, for enhanced predictive accuracy using laboratory parameters.
To compare the performance of the proposed ML model against conventional classification methods.

Main Methods:

Utilized an open-access diabetes dataset comprising patient demographics, laboratory results, and clinical outcomes.
Employed data preprocessing techniques including cleaning, normalization, and feature extraction using Adaptive Tree-Structured Parzen Estimator (ATPE) and XGBoost.
Implemented and evaluated the XGBoost algorithm for high-risk patient prediction.

Main Results:

The proposed XGBoost model achieved 83% accuracy, 80% precision, 78% recall, and a 78% F1 score.
The model demonstrated superior performance compared to traditional classification models in distinguishing high-risk patients.
Correlation and confusion matrix analyses confirmed the model's effectiveness in patient stratification.

Conclusions:

Integrating ML-based risk classification with laboratory data significantly improves predictive accuracy and patient stratification for diabetes.
The study highlights the potential of optimized XGBoost for clinical decision-making in diabetes risk assessment.
Future directions include incorporating real-time data and expanding ML applications to other diseases.