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Updated: Jun 17, 2025

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

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

Published on: January 11, 2020

Individualized Machine-learning-based Clinical Assessment Recommendation System.

Devin Setiawan¹, Yumiko Wiranto², Jeffrey M Girard²

¹The University of Kansas, Department of Electrical Engineering and Computer Science, 1415 Jayhawk Blvd. Lawrence, KS 66045.

Medrxiv : the Preprint Server for Health Sciences

|August 7, 2024

Summary

This summary is machine-generated.

The Individualized Clinical Assessment Recommendation System (iCARE) improves diagnostic accuracy by personalizing feature selection for patients. This machine learning framework enhances predictions when individual patient data offers unique insights.

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Area of Science:

Machine learning applications in healthcare
Personalized medicine and diagnostics
Clinical decision support systems

Background:

Traditional clinical assessments often lack individualization, using standardized procedures that may not suit diverse patient needs.
Personalized diagnosis in early disease stages can offer significant benefits.
There is a need for frameworks that address individualized feature selection to improve diagnostic accuracy.

Purpose of the Study:

To develop a machine learning framework for individualized feature addition in clinical assessments.
To enhance diagnostic accuracy by tailoring feature selection to individual patient characteristics.
To compare the performance of an individualized approach against a global approach.

Main Methods:

The Individualized Clinical Assessment Recommendation System (iCARE) was developed.
iCARE utilizes locally weighted logistic regression and Shapley Additive Explanations (SHAP) value analysis.
Performance was evaluated on synthetic and real-world datasets (early diabetes, heart failure) and compared to a Global approach using accuracy and AUC metrics.

Main Results:

iCARE significantly enhanced predictive accuracy and AUC on synthetic datasets 1-3 and the early diabetes dataset.
In synthetic dataset 1, iCARE achieved 0.999 accuracy and 1.000 AUC, compared to the Global approach's 0.689 accuracy and 0.639 AUC.
For the early diabetes dataset, iCARE improved accuracy and AUC by 1.5-3.5%; no significant advantage was observed when features lacked distinct predictive capabilities.

Conclusions:

The iCARE framework provides personalized feature recommendations that improve diagnostic accuracy.
Individualized approaches are critical in scenarios where patient characteristics significantly influence diagnostic outcomes.
iCARE enhances the precision and effectiveness of medical diagnoses through tailored feature selection.