CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration

TL;DR

This paper introduces CACTUS, an explainable and flexible machine learning tool for early classification of Age-related Macular Degeneration, improving diagnosis accuracy and interpretability over standard models.

Contribution

The paper presents CACTUS, a novel ML framework that enhances AMD classification by providing explainability, feature importance, and bias reduction, outperforming existing models.

Findings

CACTUS achieves higher accuracy than standard ML models.

It identifies key features aligned with medical knowledge.

The tool supports bias elimination and clinician feedback.

Abstract

Machine Learning (ML) is used to tackle various tasks, such as disease classification and prediction. The effectiveness of ML models relies heavily on having large amounts of complete data. However, healthcare data is often limited or incomplete, which can hinder model performance. Additionally, issues like the trustworthiness of solutions vary with the datasets used. The lack of transparency in some ML models further complicates their understanding and use. In healthcare, particularly in the case of Age-related Macular Degeneration (AMD), which affects millions of older adults, early diagnosis is crucial due to the absence of effective treatments for reversing progression. Diagnosing AMD involves assessing retinal images along with patients' symptom reports. There is a need for classification approaches that consider genetic, dietary, clinical, and demographic factors. Recently, we introduced the -Comprehensive Abstraction and Classification Tool for Uncovering Structures-(CACTUS), aimed at improving AMD stage classification. CACTUS offers explainability and flexibility, outperforming standard ML models. It enhances decision-making by identifying key factors and providing confidence in its results. The important features identified by CACTUS allow us to compare with existing medical knowledge. By eliminating less relevant or biased data, we created a clinical scenario for clinicians to offer feedback and address biases.