A Fuzzy Inference System for Disease Diagnosis Through Blood Report Analysis

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Abstract

Limited education and complexity of medical reports make it difficult to understand them. Seventy-two percent require help in understanding blood test results as research confirms. A user-friendly GUI application has been developed for this purpose, which can simplify the process of interpreting medical reports. This particular study includes medical reports like Complete Blood Count, Widal test, rapid test, and serology test. It is achieved by making use of Fuzzy Inference System that accepts user inputs and diagnoses diseases based on reported results. The fuzzy model dataset consists of 33 rows and 12 attributes. The main goal is to make medical report more understandable so that people can make better-informed decisions about their health care options. Initiatives such as this one aim at advancing healthcare literacy levels thereby improving health outcomes as well as allowing individuals to become proactive regarding their wellbeing. This fuzzy model has an impressive accuracy rate of 95.84%. This article presents a new way being used to increase the accessibility of medical report interpretation to the masses. Through employment of fuzzy logic with a friendly user interface, complex medical jargons are broken down into simple terms that could be understood easily hence enhancing patient engagement and health literacy by providing clear actionable insights from intricate medical data sets.

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Keywords

Fuzzy Inference System, Blood Report Analysis, Red Blood Cells, White Blood Cells, Platelets, Complete Blood Count, Gaussian Membership function