Improving the Correctness of Medical Diagnostics Based on Machine Learning With Coloured Petri Nets

Abstract

Advanced software and storage technologies have enabled medical facilities to record and store vast amounts of data about cancer patients. There is a strong demand for an accurate and interpretable method to perform cancer prognostic for effective treatment. Machine learning algorithms, undoubtedly, demonstrate a remarkable ability to recognize models and extract patterns from data to improve medical prognosis decision-making. But machine learning outcomes are prone to bias and inaccurate labelling. Therefore, to negate the impact of such errors in the prognostic decision-making process, the mechanism to correct such errors is in high demand. This article addresses this problem by proposing the use of Coloured Petri nets formalism to ensure the correctness of the machine learning based prognostic process. Use of formalism makes it possible to ensure that prognostic decisions are correct and understandable. Empirical results show that we have increased the accuracy of prognostic decisions by up to 90%. This research supports improved prognostic decision-making for the effective treatment and identification of cancer patients.

Keywords

• Medical diagnosis
• breast cancer prognosis
• decision making
• supervised machine learning
• decision trees
• formal verification