Preprint / Version 1

Heart Disease Classification Based on Personal Indicators

##article.authors##

  • Xuanye Wang

Keywords:

Heart Disease, Classification

Abstract

This work focuses on implementing an artificial neural network to help diagnose heart diseases based on some personal measurements, such as BMI, Diabetic, and skin cancer. Oversampling and undersampling are adopted to balance the imbalanced dataset to improve the performance of the model by eliminating the bias. The recall for diagnosing heart diseases after applying oversampling and undersampling to the dataset reached 82% and 83%, respectively.

References or Bibliography

[Bha18] Indresh Bhattacharyya. SMOTE and ADASYN ( handling

imbalanced data set ). https://medium.com/coinmonks/

smote-and-adasyn-handling-imbalanced-data-set-34f5223e167,

[CVD21] Cardiovascular diseases (cvds). https://www.who.int/news-room/

fact-sheets/detail/cardiovascular-diseases-(cvds), 2021.

[Dia20] What are neural networks? https://www.healthline.com/health/

diabetes/borderline-diabetes-know-the-signs, 2020.

[IDA21] Olufunke Rebecca Vincent Ijegwa David Acheme. Machine-learning

models for predicting survivability in covid-19 patients. 2021.

[NN20] What are neural networks? https://www.ibm.com/cloud/learn/

neural-networks, 2020.

[Pyt22] Kamil Pytlak. Personal key indicators of heart disease. https://www.kaggle.com/datasets/kamilpytlak/

personal-key-indicators-of-heart-disease, 2022.

Downloads

Posted

10-24-2022

License

Copyright (c) 2022 Xuanye Wang

Creative Commons License

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

The copyright holder for this article has granted JSR.org a license to display the article in perpetuity.