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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Processing imbalanced medical data at the data level with assisted-reproduction data as an example.

Junliang Zhu¹, Shaowei Pu¹, Jiaji He¹

¹Department of Health Statistics, School of Public Health, China Medical University, Shenyang, 110122, PR China.

|September 5, 2024

Summary

This summary is machine-generated.

Data imbalance in medical data mining impacts model reliability. This study found optimal cut-offs of 15% positive rate and 1500 samples for logistic models, recommending SMOTE and ADASYN for imbalanced datasets.

Keywords:

Imbalanced data Imbalanced data processing method Imbalanced degree Logistic model Sample size

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

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Published on: January 11, 2020

Area of Science:

Medical data mining
Machine learning in healthcare
Predictive modeling

Background:

Data imbalance is a significant challenge in medical data mining, leading to biased predictive models.
Effective strategies are needed to mitigate the impact of imbalanced data on classification model performance.

Purpose of the Study:

Quantify effects of data imbalance and sample size on model performance.
Identify optimal cut-off values for positive rates and sample sizes.
Evaluate methods for enhancing model accuracy in imbalanced and small sample size scenarios.

Main Methods:

Collected medical records from assisted reproductive treatment.
Used random forest for variable screening.
Constructed datasets with varying imbalance degrees and sample sizes to compare logistic regression models.
Applied SMOTE, ADASYN, OSS, and CNN for imbalance treatment.

Main Results:

Logistic model performance improved beyond 10% positive rate and 1200 samples.
Optimal cut-offs identified as 15% positive rate and 1500 samples for model stability.
SMOTE and ADASYN significantly enhanced classification performance in imbalanced, small datasets.

Conclusions:

A positive rate of 15% and sample size of 1500 are optimal for stable logistic model performance.
SMOTE and ADASYN are recommended for improving balance and accuracy in low positive rate and small sample size datasets.