Annotated Summary

References:

Castellanos-Garzon, J., Costa, E., Jose, L. J. S., & Corchado, J. M. (2019). An evolutionary framework for machine learning applied to medical data. Knowledge-Based Systems, 185,  Retrieved from https://www.sciencedirect.com/science/article/pii/S0950705119304046

This article focuses on applications of Machine Learning (ML), a form of Artificial Intelligence (AI), in medicine. An increase in demand for medical data is inevitable as the appearance of ML in the medical industry increases in future. One major challenge in computational medicine is the complexity of transforming data into a medical judgement. An evolutionary framework based on rule-based classifiers programming is then introduced. Based on the rules of form using "IF" and "THEN" conditions, patterns can then be classified easily from the search inputs.  This allows the framework to achieve rule-based classifiers. With such rules set in place, the framework which is based on an evolutionary algorithm, is then capable of correctly classifying certain set of patterns.

The article also provides an insight of how ML can be integrated into our system, Medi-Claw. While the research article provides a framework based on only a single logic system, the article still show the full potential of such a design as compared to frameworks based on other evaluation methods. The framework, which is based on an algorithm that is capable of transforming big data into a medical judgement, sits well within the field of operation for Medi-Claw. With the capabilities of performing analysis on the medical data, Medi-Claw would then have the ability to make its' own medical judgement, allowing it to perform low-risk, minor surgery, suturing.

REVISED: 30 March 2020