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

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An Unsupervised Error Detection Methodology for Detecting Mislabels in Healthcare Analytics.

Pei-Yuan Zhou¹, Faith Lum¹, Tony Jiecao Wang¹

¹Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Bioengineering (Basel, Switzerland)

|August 29, 2024

Summary

This summary is machine-generated.

This study introduces an unsupervised method for detecting abnormal samples in medical datasets. The Pattern Discovery and Disentanglement (PDD) model improves data quality, enhancing clinical decision-making and classification accuracy.

Keywords:

error detection healthcare data analysis pattern discovery and disentanglement unsupervised learning

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Area of Science:

Healthcare Data Analytics
Machine Learning in Medicine
Clinical Informatics

Background:

Medical datasets often suffer from imbalanced classes and errors from subjective testing and clinical variability.
Poor data quality negatively impacts classification accuracy and reliability, hindering effective clinical decision-making.

Purpose of the Study:

To propose an unsupervised error detection method for improving the quality of medical datasets.
To leverage the Pattern Discovery and Disentanglement (PDD) model for identifying and removing abnormal samples.

Main Methods:

Utilized the Pattern Discovery and Disentanglement (PDD) model to discover statistically significant association patterns in large datasets.
Applied the algorithm to the eICU Collaborative Research Database for sepsis risk assessment.
Clustered samples in an unsupervised manner and detected abnormal data points.

Main Results:

The proposed algorithm outperformed K-Means clustering by 38% on full datasets and 47% on reduced datasets.
Removing abnormal samples using the error detection approach improved the accuracy of multiple supervised classifiers by an average of 4%.

Conclusions:

The developed algorithm offers a robust and practical solution for unsupervised clustering and error detection in healthcare data.
Improved data quality through automated error detection can significantly enhance the reliability of clinical risk assessment and predictive modeling.