Cardiac Disease Diagnosis Using K-Nearest Neighbor Algorithm: A Study on Heart Failure Clinical Records Dataset

Authors

  • Vitória Stefane Souza Laboratory of Computational Intelligence, Robotics and Optimization (LICRO), Federal Institute of Education, Science and Technology of Triângulo Mineiro (IFTM) Campus Patrocínio MG, Brazil https://orcid.org/0000-0001-9057-9738
  • Danielli Araújo Lima Laboratory of Computational Intelligence, Robotics and Optimization (LICRO), Federal Institute of Education, Science and Technology of Triângulo Mineiro (IFTM) Campus Patrocínio MG, Brazil https://orcid.org/0000-0003-0324-6690

DOI:

https://doi.org/10.47852/bonviewAIA42022045

Keywords:

K-Nearest neighbors, classification algorithm, machine learning, expert system, heart failure, correlation filter, artificial intelligence as a service

Abstract

This article introduces an approach to diagnose heart diseases utilizing the K-Nearest Neighbor algorithm and diverse correlation filters for selecting the most pertinent attributes. Results high- light that meticulous filter selection enhances survival predictions in patients with heart diseases. Employing K = 5 and correlation filter CF = 0.1, key attributes for classification were identified as anemia, high blood pressure, serum creatinine, and sex. Omitting the 'time' attribute led to information loss but was crucial to prevent biases and generalize predictions across various clinical scenarios. Utilizing these classification parameters, we designed an Android mobile application called “Heart Info System”, functioning as an artificial intelligence service. It employs the K-Nearest Neighbor algorithm with optimal parameters to evaluate the probability of survival in the progression of heart disease. The main activity of the application retrieves data from a Firebase database. While the study results show promise, the accuracy of the application may be influenced by inaccurate or incomplete input data. Nevertheless, this application has the potential to improve the early detection of heart diseases, paving the way for life-saving interventions.

 

Received: 11 November 2023 | Revised: 18 April 2024 | Accepted: 25 April 2024

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

 

Data Availability Statement

The data that support the findings of this study are openly available in UCI Machine Learning Repository (Heart Failure Clinical Records Dataset) at https://archive.ics.uci.edu/dataset/519/heart+failure+clinical+records DOI: https://www.doi.org/10.24432/C5Z89R.

 

Author Contribution Statement

Vitória Stefane Souza: Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Danielli Araújo Lima: Conceptualization, Methodology, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.


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Published

2024-04-30

Issue

Online First

Section

Online First Articles

License

Copyright (c) 2024 Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Stefane Souza, V., & Araújo Lima, D. (2024). Cardiac Disease Diagnosis Using K-Nearest Neighbor Algorithm: A Study on Heart Failure Clinical Records Dataset. Artificial Intelligence and Applications. https://doi.org/10.47852/bonviewAIA42022045