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Reliable confidence measures for medical diagnosis with evolutionary algorithms
Antonis Lambrou1, Harris Papadopoulos, Alex Gammerman
1Computer Learning Research Centre and the Department of Computer Science, Royal Holloway, University of London, Surrey, UK. A.Lambrou@cs.rhul.ac.uk
Summary
This study introduces a novel Conformal Predictor (CP) using a genetic algorithm (GA) for medical diagnosis. The method provides accurate predictions with reliable confidence measures for breast and ovarian cancer detection.
Area of Science:
- Machine Learning
- Medical Diagnostics
- Bioinformatics
Background:
- Conformal Predictors (CPs) offer valuable confidence measures for machine-generated medical diagnoses.
- Risk assessment in medical predictions is crucial for patient outcomes.
- Integrating classical machine learning with CPs is an active research area.
Purpose of the Study:
- To develop a human-readable Conformal Predictor (CP) using a genetic algorithm (GA).
- To evaluate the CP's performance in real-world medical diagnostic datasets.
- To assess the reliability and utility of confidence measures provided by the CP.
Main Methods:
- A genetic algorithm (GA) was employed to evolve rule sets for Conformal Prediction (CP).
- The proposed CP method was applied to two distinct medical diagnostic datasets: breast cancer and ovarian cancer.
- Performance was benchmarked against classical machine learning techniques.
Main Results:
- The GA-based CP achieved accuracy comparable to established methods on both breast and ovarian cancer datasets.
- The CP successfully generated reliable and interpretable confidence measures for each prediction.
- The human-readable rule sets facilitated understanding of the diagnostic process.
Conclusions:
- The proposed genetic algorithm-based Conformal Predictor (CP) is a viable and effective tool for medical diagnosis.
- The method provides accurate predictions alongside trustworthy confidence intervals, aiding clinical decision-making.
- This approach enhances interpretability in machine learning for critical applications like cancer detection.
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