A Comparative Analysis using various algorithm Approaches to Enhance Heart Disease Prognosis





Heart Prognosis, Machine Learning, Data Mining, Naive Bayes, SGD


INTRODUCTION: Modern advancements in technology and data science have propelled the healthcare industry towards developing more accurate disease prognostic prediction models. Heart disease, being a leading cause of mortality globally, is a critical area of focus. This study delves into enhancing heart disease prognosis through a comprehensive exploration of various algorithmic approaches.

OBJECTIVES: The objective of this paper is to compare and analyze different algorithmic techniques to improve heart disease prognosis using a dataset comprising data from over thirty thousand individuals obtained through Kaggle.

METHODS: Techniques derived from social network analysis are employed to conduct this research. Data preprocessing, feature engineering, algorithm selection (including Stochastic Gradient Descent, AdaBoosting, Support Vector Machine, and Naive Bayes), hyperparameter tuning, model evaluation, and visualization are part of the systematic research process.

RESULTS: The main results obtained in this paper include the identification of Naive Bayes as the most effective model for heart disease prognosis, followed by AdaBoosting, SVM, and Stochastic Gradient Descent. Performance evaluation metrics such as AUC, CA, F1, Precision, and Recall demonstrate the efficacy of these models.

CONCLUSION: This research contributes to improving heart disease prognosis by leveraging algorithmic techniques and thorough analysis. The study envisions integrating the developed model into healthcare systems for widespread access to accurate heart disease prediction, with future plans to enhance data collection and model improvement for better outcomes.


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Dini, Frank L., et al. ”Right ventricular failure in left heart disease: from pathophys iology to clinical manifestations and prognosis.” Heart Failure Reviews 28.4 (2023): 757-766. DOI: https://doi.org/10.1007/s10741-022-10282-2

Ng, Mei Li, et al. ”Novel Oxidative Stress Biomarkers with Risk Prognosis Values in Heart Failure.” Biomedicines 11.3 (2023): 917. 3. Gordon, Jonathan, et al. ”Oxygen uptake efficiency slope and prognosis in heart failure with reduced ejection fraction.” The American Journal of Cardiology 201 (2023): 273-280. DOI: https://doi.org/10.1016/j.amjcard.2023.06.033

Nakao, Yoko M., et al. ”Prognosis, characteristics, and provision of care for patients with the unspecified heart failure electronic health record phenotype: a population based linked cohort study of 95262 individuals.” eClinicalMedicine 63 (2023): 102164. DOI: https://doi.org/10.1016/j.eclinm.2023.102164

Tian, Jing, et al. ”Machine learning prognosis model based on patient-reported out comes for chronic heart failure patients after discharge.” Health and Quality of Life Outcomes 21.1 (2023): 31. DOI: https://doi.org/10.1186/s12955-023-02109-x

Bhushan, Megha, Akkshat Pandit, and Ayush Garg. ”Machine learning and deep learning techniques for the analysis of heart disease: a systematic literature review, open challenges and future directions.” Artificial Intelligence Review (2023): 1-52. DOI: https://doi.org/10.1007/s10462-023-10493-5

Ferrari, Margaret Rose. Unsupervised Machine Learning Methods Applied Towards the Understanding of Central Venous Flow and Prognosis in Single Ventricle Heart Disease. Diss. University of Colorado at Denver, 2023.

Tong, Rui, Zhongsheng Zhu, and Jia Ling. ”Comparison of linear and non-linear machine learning models for time-dependent readmission or mortality prediction among hospitalized heart failure patients.” Heliyon 9.5 (2023). DOI: https://doi.org/10.1016/j.heliyon.2023.e16068

Ahmed, Rehan, Maria Bibi, and Sibtain Syed. ”Improving Heart Disease Prediction Accuracy Using a Hybrid Machine Learning Approach: A Comparative study of SVM and KNN Algorithms.” International Journal of Computations, Information and Manufacturing (IJCIM) 3.1 (2023): 49-54. DOI: https://doi.org/10.54489/ijcim.v3i1.223

Bizimana, Pierre Claver, et al. ”An Effective Machine Learning-Based Model for an Early Heart Disease Prediction.” BioMed Research International 2023 (2023). DOI: https://doi.org/10.1155/2023/3531420

Olsen, Cameron R., et al. ”Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure.” American Heart Journal 229 (2020): 1-17. DOI: https://doi.org/10.1016/j.ahj.2020.07.009

Zhong, Zhihua, et al. ”Machine learning prediction models for prognosis of critically ill patients after open-heart surgery.” Scientific Reports 11.1 (2021): 3384. DOI: https://doi.org/10.1038/s41598-021-83020-7

Miao, Kathleen H., and Julia H. Miao. ”Coronary heart disease diagnosis using deep neural networks.” international journal of advanced computer science and applications 9.10 (2018). DOI: https://doi.org/10.14569/IJACSA.2018.091001

Angraal, Suveen, et al. ”Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction.” JACC: Heart Failure 8.1 (2020): 12-21. DOI: https://doi.org/10.1016/j.jchf.2019.06.013

Gao, Yifeng, et al. ”Deep learning-based prognostic model using non-enhanced car diac cine MRI for outcome prediction in patients with heart failure.” European Radiology (2023): 1-11. 10 DOI: https://doi.org/10.1007/s00330-023-09785-9

Shashikant, R., and P. Chetankumar. ”Predictive model of cardiac arrest in smok ers using machine learning technique based on Heart Rate Variability parameter.” Applied Computing and Informatics 19.3/4 (2023): 174-185. DOI: https://doi.org/10.1016/j.aci.2019.06.002

Moreno-Sanchez, Pedro A.”Improvement of a prediction model for heart failure sur vival through explainable artificial intelligence.” Frontiers in Cardiovascular Medicine 10 (2023) DOI: https://doi.org/10.3389/fcvm.2023.1219586

Mishra, Saurav. ”A comparative study for time-to-event analysis and survival prediction for heart failure condition using machine learning techniques.” Jour nal of Electronics, Electromedical Engineering, and Medical Informatics 4.3 (2022): 115-134. DOI: https://doi.org/10.35882/jeeemi.v4i3.225




How to Cite

Ramineni A, Konda R, J J, Sannapareddy D, Konduri S. A Comparative Analysis using various algorithm Approaches to Enhance Heart Disease Prognosis. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Apr. 2 [cited 2024 Apr. 25];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5615