Software Reuse in Medical Database for Cardiac Patients using Pearson Family Equations

TL;DR

This paper presents a method for medical database reuse in cardiology using Pearson distribution and coupling techniques to improve patient treatment recommendations, especially in remote areas.

Contribution

It introduces a novel approach combining Pearson Type I distribution and coupling methodology for effective software reuse in medical data analysis.

Findings

Enhanced clustering of cardiology data using Pearson distribution

Improved patient similarity assessment through coupling methodology

Potential for better treatment decisions in remote healthcare settings

Abstract

Software reuse is a subfield of software engineering that is used to adopt the existing software for similar purposes. Reuse Metrics determine the extent to which an existing software component is reused in new software with an objective to minimize the errors and cost of the new project. In this paper, medical database related to cardiology is considered. The Pearson Type I Distribution is used to calculate the probability density function (pdf) and thereby utilizing it for clustering the data. Further, coupling methodology is used to bring out the similarity of the new patient data by comparing it with the existing data. By this, the concerned treatment to be followed for the new patient is deduced by comparing with that of the previous patients case history. The metrics proposed by Chidamber and Kemerer are utilized for this purpose. This model will be useful for the medical field through software, particularly in remote areas.