Comprehensive feature selection for classifying the treatment outcome of high-intensity ultrasound therapy in uterine fibroids

TL;DR

This study employs multiple feature selection methods to identify key clinical parameters for predicting high-intensity focused ultrasound treatment outcomes in uterine fibroids, demonstrating that feature choice significantly impacts classification accuracy.

Contribution

It introduces a comprehensive approach combining 14 feature selection methods with SVC to improve outcome prediction in uterine fibroid HIFU therapy.

Findings

Maximum F1-micro score of 0.63 achieved with top features

Top features include fibroid size, location, and patient symptoms

Feature selection greatly influences prediction performance

Abstract

The study aim was to utilise multiple feature selection methods in order to select the most important parameters from clinical patient data for high-intensity focused ultrasound (HIFU) treatment outcome classification in uterine fibroids. The study was retrospective using patient data from 66 HIFU treatments with 89 uterine fibroids. A total of 39 features were extracted from the patient data and 14 different filter-based feature selection methods were used to select the most informative features. The selected features were then used in a support vector classification (SVC) model to evaluate the performance of these parameters in predicting HIFU therapy outcome. The therapy outcome was defined as non-perfused volume (NPV) ratio in three classes: <30%, 30-80% or >80%. The ten most highly ranked features in order were: fibroid diameter, subcutaneous fat thickness, fibroid volume, fibroid distance, Funaki type I, fundus location, gravidity, Funaki type III, submucosal fibroid type and urinary symptoms. The maximum F1-micro classification score was 0.63 using the top ten features from Mutual Information Maximisation (MIM) and Joint Mutual Information (JMI) feature selection methods. Classification performance of HIFU therapy outcome prediction in uterine fibroids is highly dependent on the chosen feature set which should be determined prior using different classifiers.