Heart Disease Prediction with Decision Tree
Abstract
Heart disease remains a major global health issue, which emphasizes the need for accurate prediction models to aid early diagnosis and effective intervention. This study explores the use of a Decision Tree algorithm to predict heart disease. The dataset used consists of 272 entries, with seven key variables including age, sex, blood pressure, cholesterol, fasting blood sugar, maximum heart rate, and ST depression. The data underwent preprocessing to handle missing values and convert categorical data into numerical format. The model was trained with 80% of the data and tested with the remaining 20%. Evaluation metrics such as accuracy, precision, recall, and f1-score, were used to evaluate the model’s performance. The results demonstrated the model’s efficacy, achieving an accuracy of 81.48%, a recall of 82.93%, a precision of 91.89%, and an f1-score of 87.18%. These results highlight the potential of the Decision Tree Algorithm in heart disease prediction, particularly for its simplicity and interpretability. Despite the study’s limitations, such as the small dataset size that could affect generalizability, this study demonstrates significant predictive potential and a strong foundation for future work. Future research should explore alternative machine learning algorithms to improve prediction accuracy and enhance the model’s robustness for real-world application.
References
[2] N. A. Baghdadi, S. M. Farghaly Abdelaliem, A. Malki, I. Gad, A. Ewis, and E. Atlam, “Advanced machine learning techniques for cardiovascular disease early detection and diagnosis,” J Big Data, vol. 10, no. 1, Dec. 2023, doi: 10.1186/s40537-023-00817-1.
[3] L. I. Santos et al., “Decision tree and artificial immune systems for stroke prediction in imbalanced data,” Expert Syst Appl, vol. 191, Apr. 2022, doi: 10.1016/j.eswa.2021.116221.
[4] C. M. Bhatt, P. Patel, T. Ghetia, and P. L. Mazzeo, “Effective Heart Disease Prediction Using Machine Learning Techniques,” Algorithms, vol. 16, no. 2, Feb. 2023, doi: 10.3390/a16020088.
[5] M. Ozcan and S. Peker, “A classification and regression tree algorithm for heart disease modeling and prediction,” Healthcare Analytics, vol. 3, Nov. 2023, doi: 10.1016/j.health.2022.100130.
[6] Rishi Damarla, “Heart Disease Prediction,” https://www.kaggle.com/datasets/rishidamarla/heart-disease-prediction/data.
[7] D. K. Arnett et al., “2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines,” Sep. 10, 2019, NLM (Medline). doi: 10.1161/CIR.0000000000000678.
[8] T. G. Richardson et al., “Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis,” PLoS Med, vol. 17, no. 3, Mar. 2020, doi: 10.1371/JOURNAL.PMED.1003062.
[9] Y. Liang and C. Guo, “Heart failure disease prediction and stratification with temporal electronic health records data using patient representation,” Biocybern Biomed Eng, vol. 43, no. 1, pp. 124–141, Jan. 2023, doi: 10.1016/j.bbe.2022.12.008.
[10] N. Chandrasekhar and S. Peddakrishna, “Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization,” Processes, vol. 11, no. 4, Apr. 2023, doi: 10.3390/pr11041210.
[11] M. Ozcan and S. Peker, “A classification and regression tree algorithm for heart disease modeling and prediction,” Healthcare Analytics, vol. 3, Nov. 2023, doi: 10.1016/j.health.2022.100130.
[12] V. Chole, M. Thawakar, M. Choudhari, S. Chahande, S. Verma, and A. Pimpalkar, “Enhancing heart disease risk prediction with GdHO fused layered BiLSTM and HRV features: A dynamic approach,” Biomed Signal Process Control, vol. 95, Sep. 2024, doi: 10.1016/j.bspc.2024.106470.
[13] M. M. Ali, B. K. Paul, K. Ahmed, F. M. Bui, J. M. W. Quinn, and M. A. Moni, “Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison,” Comput Biol Med, vol. 136, Sep. 2021, doi: 10.1016/j.compbiomed.2021.104672.
[14] F. Handayani et al., “JEPIN (Jurnal Edukasi dan Penelitian Informatika) Komparasi Support Vector Machine, Logistic Regression Dan Artificial Neural Network dalam Prediksi Penyakit Jantung,” vol. 7, 2021.
[15] M. M. Ali et al., “A machine learning approach for risk factors analysis and survival prediction of Heart Failure patients,” Healthcare Analytics, vol. 3, Nov. 2023, doi: 10.1016/j.health.2023.100182.
[16] G. Manikandan, B. Pragadeesh, V. Manojkumar, A. L. Karthikeyan, R. Manikandan, and A. H. Gandomi, “Classification models combined with Boruta feature selection for heart disease prediction,” Inform Med Unlocked, vol. 44, Jan. 2024,
doi: 10.1016/j.imu.2023.101442.
[17] D. Deepika and N. Balaji, “Effective heart disease prediction using novel MLP-EBMDA approach,” Biomed Signal Process Control, vol. 72, Feb. 2022,
doi: 10.1016/j.bspc.2021.103318.
Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.