An Adaptive parameter free data mining approach for healthcare application

TL;DR

This paper presents an adaptive, parameter-free data mining approach using clustering algorithms like K-means and D-stream to assess health status based on historical and real-time biomedical data, aiming to improve healthcare diagnostics.

Contribution

It introduces an adaptive, parameter-free data mining framework applying clustering algorithms for healthcare assessment, comparing their effectiveness on biomedical data.

Findings

D-stream overcomes K-Means drawbacks in healthcare data clustering

Both algorithms effectively classify health status using biomedical data

Performance measures indicate D-stream's superior efficiency and accuracy

Abstract

In today's world, healthcare is the most important factor affecting human life. Due to heavy work load it is not possible for personal healthcare. The proposed system acts as a preventive measure for determining whether a person is fit or unfit based on person's historical and real time data by applying clustering algorithms like K-means and D-stream. The Density-based clustering algorithm i.e. the D-stream algorithm overcomes drawbacks of K-Means algorithm. By calculating their performance measures we finally find out effectiveness and efficiency of both the algorithms. Both clustering algorithms are applied on patient's bio-medical historical database. To check the correctness of both the algorithms, we apply them on patient's current bio-medical data.