Deriving Hematological Disease Classes Using Fuzzy Logic and Expert Knowledge: A Comprehensive Machine Learning Approach with CBC Parameters

TL;DR

This paper presents a novel machine learning approach combining fuzzy logic rules and expert knowledge to improve hematological disease classification using CBC parameters, demonstrating high accuracy and nuanced diagnostics.

Contribution

It introduces a hybrid fuzzy logic and Random Forest method guided by expert knowledge for more accurate hematological disease classification.

Findings

High diagnostic accuracy achieved with the proposed method

Fuzzy logic enhances the handling of diagnostic ambiguity

Outperforms traditional diagnostic techniques

Abstract

In the intricate field of medical diagnostics, capturing the subtle manifestations of diseases remains a challenge. Traditional methods, often binary in nature, may not encapsulate the nuanced variances that exist in real-world clinical scenarios. This paper introduces a novel approach by leveraging Fuzzy Logic Rules to derive disease classes based on expert domain knowledge from a medical practitioner. By recognizing that diseases do not always fit into neat categories, and that expert knowledge can guide the fuzzification of these boundaries, our methodology offers a more sophisticated and nuanced diagnostic tool. Using a dataset procured from a prominent hospital, containing detailed patient blood count records, we harness Fuzzy Logic Rules, a computational technique celebrated for its ability to handle ambiguity. This approach, moving through stages of fuzzification, rule application, inference, and ultimately defuzzification, produces refined diagnostic predictions. When combined with the Random Forest classifier, the system adeptly predicts hematological conditions using Complete Blood Count (CBC) parameters. Preliminary results showcase high accuracy levels, underscoring the advantages of integrating fuzzy logic into the diagnostic process. When juxtaposed with traditional diagnostic techniques, it becomes evident that Fuzzy Logic, especially when guided by medical expertise, offers significant advancements in the realm of hematological diagnostics. This paper not only paves the path for enhanced patient care but also beckons a deeper dive into the potentialities of fuzzy logic in various medical diagnostic applications.