Heart Attack Prediction With Svm

Hanna Willa Dhany

Abstract


Heart attacks are one of the leading causes of death worldwide, making them an important focus in medical prevention and treatment efforts. Early detection of heart attack risk is crucial to provide timely medical intervention and prevent further complications. In this study, we used Support Vector Machine (SVM), one of the effective machine learning algorithms, to predict heart attacks. SVM works by finding the optimal hyperplane that separates data into different classes. We analyzed various risk factors such as age, gender, blood pressure, cholesterol levels, and medical history to predict the likelihood of a heart attack. The results showed that the developed SVM model had an accuracy rate of 91.80%, indicating that SVM can be a reliable prediction tool. This model is expected to help medical personnel make better decisions and provide more personalized care to high-risk patients, as well as contribute significantly to the treatment and prevention of heart attacks.

Keywords


Heart attacks, Support Vector Machine, age, gender, blood pressure, cholesterol levels, and medical history.

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References


A. Borji, A. Seif, and T. H. Hejazi, “An efcient method for detection of Alzheimer’s disease using high-dimensional PET scan images,” pp. 729–749, 2023.

Das, S.; Sharma, R.; Gourisaria, M.; Rautaray, S.; Pandey, M. Heart disease detection using core machine learning and deep learning techniques: A comparative study. Int. J. Emerg. Technol. 2020, 11, 531–538.

Deo, R.C. Machine learning in medicine. Circulation 2015, 132, 1920–1930.

Detrano, R.; Janosi, A.; Steinbrunn, W.; Pfisterer, M.; Schmid, J.J.; Sandhu, S.; Guppy, K.H.; Lee, S.; Froelicher, V. International application of a new probability algorithm for the diagnosis of coronary artery disease. Am. J. Cardiol. 1989, 64, 304–310.

Dinar, A.M.; Zain, A.M.; Salehuddin, F. Utilizing of CMOS ISFET sensors in DNA applications detection: A systematic review. J. Adv. Res. Dyn. Control Syst. 2018, 10, 569–583.

Diwakar, M.; Tripathi, A.; Joshi, K.; Memoria, M.; Singh, P. Latest trends on heart disease prediction using machine learning and image fusion. Mater. Today Proc. 2021, 37, 3213–3218.

Elhoseny, M.; Mohammed, M.A.; Mostafa, S.A.; Abdulkareem, K.H.; Maashi, M.S.; Garcia-Zapirain, B.; Mutlag, A.A.; Maashi, M.S. A new multi-agent feature wrapper machine learning approach for heart disease diagnosis. Comput. Mater. Contin 2021, 67, 51–71.

Gennari, J.H.; Langley, P.; Fisher, D. Models of incremental concept formation. Artif. Intell. 1989, 40, 11–61

Hasan, T.T.; Jasim, M.H.; Hashim, I.A. FPGA Design and Hardware Implementation of Heart Disease Diagnosis System Based on NVG-RAM Classifier. In Proceedings of the 2018 Third Scientific Conference of Electrical Engineering (SCEE), Baghdad, Iraq, 19–20 December 2018; pp. 33–38.

Hu, G.; Root, M.M. Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings. Eur. J. Prev. Cardiol. 2005, 12, 459–464. [Google Scholar] [CrossRef] [PubMed]

J. Ma, D. Lei, Z. Ren, C. Tan, D. Xia, and H. Guo, “Automated machine learning-based landslide susceptibility mapping for the three Gorges reservoir area, China,” Mathematical Geo sciences, vol. 2, 2023.

Janosi, A.; Steinbrunn, W.; Pfisterer, M.; Detrano, R. UCI Machine Learning Repository: Heart Disease Dataset [Online]. Available online: https://archive-beta.ics.uci.edu/ml/datasets/heart+disease.

Javid, I.; Khalaf, A.; Ghazali, R. Enhanced accuracy of heart disease prediction using machine learning and recurrent neural networks ensemble majority voting method. Int. J. Adv. Comput. Sci. Appl. 2020, 11, 540–551.

Jawalkar, A. P., Swetcha, P., Manasvi, N., Sreekala, P., Aishwarya, S., Bhavani, K. D., & Anjani, P. (2023). Early prediction of heart disease with data analysis using supervised learning with stochastic gradient boosting. Journal of Engineering and Applied Science, 70, Article number: 122.

M. Rajabi and R. P. R. Hasanzadeh, “A modifed adaptive hysteresis smoothing approach for image denoising based on spatial domain redundancy,” Sens Imaging, vol. 22, no. 1, p. 42, 2021.

M. Rajabi, H. Golshan, and R. P. Hasanzadeh, “Non-local adaptive hysteresis despeckling approach for medical ultra sound images,” Biomedical Signal Processing and Control, vol. 85, Article ID 105042, 2023.

Mohammed, M.A.; Abdulkareem, K.H.; Al-Waisy, A.S.; Mostafa, S.A.; Al-Fahdawi, S.; Dinar, A.M.; Alhakami, W.; Abdullah, B.A.Z.; Al-Mhiqani, M.N.; Alhakami, H.; et al. Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods. IEEE Access 2020, 8, 99115–99131.

Muhsen, D.K.; Khairi, T.W.A.; Alhamza, N.I.A. Machine Learning System Using Modified Random Forest Algorithm. In Intelligent Systems and Networks, Singapore; Tran, D.-T., Jeon, G., Nguyen, T.D.L., Lu, J., Xuan, T.-D., Eds.; Springer: Singapore, 2021; pp. 508–515.

Mythili, T.; Mukherji, D.; Padalia, N.; Naidu, A. A heart disease prediction model using SVM-decision trees-logistic regression (SDL). Int. J. Comput. Appl. 2013, 68, 0975–8887.

Nasser, A.R.; Hasan, A.M.; Humaidi, A.J.; Alkhayyat, A.; Alzubaidi, L.; Fadhel, M.A.; Santamaría, J.; Duan, Y. IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes. Electronics 2021, 10, 2719. Available online: https://www.mdpi.com/2079-9292/10/21/2719

Rahman, A.U.; Saeed, M.; Mohammed, M.A.; Jaber, M.M.; Garcia-Zapirain, B. A novel fuzzy parameterized fuzzy hypersoft set and riesz summability approach based decision support system for diagnosis of heart diseases.

Rahman, A.U.; Saeed, M.; Mohammed, M.A.; Krishnamoorthy, S.; Kadry, S.; Eid, F. An Integrated Algorithmic MADM Approach for Heart Diseases’ Diagnosis Based on Neutrosophic Hypersoft Set with Possibility Degree-Based Setting. Life 2022, 12, 729.

Singh Rajpoot, V., Vishwakarma, S., Sahu, S., & Kesharwani, Y. (2024). Enhancing Heart Attack Prediction with Machine Learning. International Journal of Novel Research and Development (IJNRD), 9(6), 1-10.

Soni, M.; Gomathi, S.; Kumar, P.; Churi, P.P.; Mohammed, M.A.; Salman, A.O. Hybridizing Convolutional Neural Network for Classification of Lung Diseases. Int. J. Swarm Intell. Res. (IJSIR) 2022, 13, 1–15.

Wah, T.Y.; Mohammed, M.A.; Iqbal, U.; Kadry, S.; Majumdar, A.; Thinnukool, O. Novel DERMA fusion technique for ECG heartbeat classification. Life 2022, 12, 842.

Y. Long, W. Li, R. Huang, Q. Xu, B. Yu, and G. Liu, “A comparative study of supervised classifcation methods for investigating landslide evolution in the Mianyuan River Basin, China,” Journal of Earth Sciences, vol. 34, no. 2, pp. 316 329,2023.

Z. Jia, J. Peng, Q. Lu et al., “A comprehensive method for the risk assessment of ground fssures: case study of the eastern weihe basin,” Journal of Earth Sciences, vol. 34, no. 6, pp. 1892–1907, 2023.

Z. Liu, J. Ma, D. Xia et al., “Toward the reliable prediction of reservoir landslide displacement using earthworm optimi zation algorithm-optimized support vector regression (EOA SVR),” Natural Hazards, vol. 14, 2023.

Supiyandi, S., Iqbal, M., Purba, R. B., & Rizal, C. (2023). Development of logic gateway and network learning applications using augmented reality for computer architecture addie method curriculum. Prosiding universitas dharmawangsa, 3(1), 605–613

C. Rizal., Erni Marlina Saari. (2024). Leveraging Artificial Intelligence for Sustainable Software Maintenance: A Case Study Approach. Proceedings The 2nd Annual Dharmawangsa International Conference: “Digital Technology and Environmental Awareness in Promoting Sustainable Behavior In Society 5.0. vol. 1, no. 1; pp. 1-12.


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