Machine Learning based Heart Disease Prediction Task

Yijie Zhang

doi:10.54097/hset.v65i.11359

Authors

Yijie Zhang

DOI:

https://doi.org/10.54097/hset.v65i.11359

Keywords:

Machine learning, heart disease, random forest.

Abstract

Heart disease is a threat to health condition that has plagued human beings for a long time. The cause of heart disease is complex, and the symptoms are various, which brings many difficulties to the diagnosis and treatment process. With the introduction of machine learning, these algorithms can be used to model the pathogenesis of heart disease and related parameters to complete the initial diagnosis of heart disease. Compared with traditional artificial induction into the causes of heart disease, machine learning-based methods tend to be more efficient and accurate. However, different models may have different performances in this data set since they operate in different ways, so their performances differ from each other. Model accuracy in this task needs to be measured and compared with the same standard. This paper finds that the Random Forest model fits the heart disease prediction task best and has the greatest potential to be optimized. Studying these models which has the most prediction effect on heart disease is valuable for solving this puzzle eventually.

Downloads

Download data is not yet available.

References

Theresa Princy. R, J.Thomas, “Human Heart Disease Prediction System using Data Mining Techniques”, International Conference on Circuit,Power and Computing Technologies [ICCPCT] (2016).

Sun Tiezheng, Yu Zehao. Heart cases based on machine learning classification prediction research [J]. Computer knowledge and technology (2021).

R. G. Franklin and B. Muthukumar, "Survey of Heart Disease Prediction and Identification using Machine Learning Approaches," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) (2020).

M. J. A. Junaid and R. Kumar, "Data Science And Its Application In Heart Disease Prediction," 2020 International Conference on Intelligent Engineering and Management (ICIEM) (2020).

Zhang Hui, Chen Dandan, Shao Jing et al. Construction, validation and comparison of metabolic syndrome risk prediction model based on KNN algorithm [J/OL]. Chongqing medical: 1-7 (2023).

Timothy M. Amado and Jennifer C. Dela Cruz "Development of Machine Learning-Based Predictive Models for Air Qualit y Monitoring and Characterization" TENCON 2018 - 2018 IEEE Region 10 Conference 2018 (2018).

L. Wei Y. Yang R. M. Nishikawa M.N. Wernick and A. Edwards "Relevance vector machine for automatic detection of clustered microcalcifications" IEEE Trans. Med. Imag. vol. 24 no.10 (2005).

Cousyn, C., Bouchard, K., Gaboury, S., and Bouchard, B. Towards Using Scientific Publications to Automatically Extract Information on Rare Diseases.Mobile Networks and Applications, 1-8 (2019).

S. Kido, Y. Hirano and N. Hashimoto, "Detection and classification of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN)," 2018 International Workshop on Advanced Image Technology (IWAIT), Chiang Mai, Thailand (2018).

Zhang Xiaoyu, Shen Chao, Lin Chenhao et al. Test and Repair for Machine Learning Model Security [J]. Acta Electronica Sinica (2022).

H. Gaßner et al. "Gait variability as digital biomarker of disease severity in huntingtons disease" Journal of neurology pp. 1-8 2020 (2020).