Machine Learning Techniques for Predication of Heart Diseases
DOI:
https://doi.org/10.29304/jqcsm.2024.16.31646Keywords:
Heart disease, Data mining, Random forest, Support Vector Machine, Confusion matrices.Abstract
Heart disease, or cardiovascular illness, encompasses a wide range of disorders affecting the cardiovascular system. One of the trickiest things to do in medicine is to make predictions about cardiovascular disease. Nowadays, heart disease claims the life of almost one person every minute. Heart disease has several causes, but one of the most pressing issues is the lack of sensitive, precise methods for early identification, which makes proper management of the condition impossible. Automating the prediction process is necessary to prevent the hazards connected with cardiac disease diagnosis and to inform the patient at an early stage due to the intricacy of the condition. Data mining is extensively utilized in healthcare to forecast the occurrence of cardiovascular illness by analyzing massive and intricate medical records. To forecast cardiac problems, researchers conduct in-depth analyses of massive amounts of medical data using a wide range of data mining and machine learning algorithms. Here, we provide several heart disease characteristics and build a model using supervised learning methods like random forest and support vector machine (SVM). The Kaggle repository contains the cardiac condition dataset that is used in this research. Predicting patients' risk of heart disease is the main objective of this investigation. Confusion matrices were used for proposed system evaluation .The findings demonstrate that Random Forest achieves the highest level of accuracy, reaching 98.54 percent.
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Seckeler, M. D., & Hoke, T. R. (2018). “The worldwide epidemiology of acute rheumatic fever and rheumatic heart disease”. Clinical epidemiology, 67-84.
Saxena, K., & Sharma, R. (2016). “Efficient heart disease prediction system”. Procedia Computer Science, 85, 962-969.
Dangare, C. S., & Apte, S. S. (2017). “Improved study of heart disease prediction system using data mining classification techniques”. International Journal of Computer Applications, 47(10), 44-48.
Shorewala, V. (2021). “Early detection of coronary heart disease using ensemble techniques”. Informatics in Medicine Unlocked, 26, 100655.
Murthy, H. N., & Meenakshi, M. (2016, November). “Dimensionality reduction using neuro-genetic approach for early prediction of coronary heart disease”. In International conference on circuits, communication, control and computing (pp. 329-332). IEEE.
Al-Saedi, K. H., & Abd Alhassn, R. A. A. (2018). Miner Alerts Module to Generate Itemsets Based on FP-Growth Algorithm Improvement. Al-Mustansiriyah Journal of Science, 29(1), 114-117.
Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., ... & Turner, M. B. (2016). Heart disease and stroke statistics—2016 update: a report from the American Heart Association. circulation, 133(4), e38-e360.
Maiga, J., & Hungilo, G. G. (2019, October). “Comparison of machine learning models in prediction of cardiovascular disease using health record data”. In 2019 international conference on informatics, multimedia, cyber and information system (ICIMCIS) (pp. 45-48). IEEE.
Soni, J., Ansari, U., Sharma, D., & Soni, S. (2019). “Predictive data mining for medical diagnosis”: An overview of heart disease prediction. International Journal of Computer Applications, 17(8), 43-48.
Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). “Can machine-learning improve cardiovascular risk prediction using routine clinical data?”. PloS one, 12(4), e0174944.
Ramalingam, V. V., Dandapath, A., & Raja, M. K. (2018). Heart disease prediction using machine learning techniques: a survey. International Journal of Engineering & Technology, 7(2.8), 684-687.
Mohan, S., Thirumalai, C., & Srivastava, G. (2019). “Effective heart disease prediction using hybrid machine learning techniques”. IEEE access, 7, 81542-81554.
Nabeel, M., Majeed, S., Awan, M. J., Muslih-ud-Din, H., Wasique, M., & Nasir, R. (2021). “Review on Effective Disease Prediction through Data Mining Techniques”. International Journal on Electrical Engineering & Informatics, 13(3).
Ramesh, T. R., Lilhore, U. K., Poongodi, M., Simaiya, S., Kaur, A., & Hamdi, M. (2022). “Predictive analysis of heart diseases with machine learning approaches”. Malaysian Journal of Computer Science, 132-148.
Alotaibi, F. S. (2019). “Implementation of machine learning model to predict heart failure disease”. International Journal of Advanced Computer Science and Applications, 10(6).
Shah, D., Patel, S., & Bharti, S. K. (2020). “Heart disease prediction using machine learning techniques”. SN Computer Science, 1, 1-6.
Shimaa Ouf, A. I. (2021). “A proposed paradigm for intelligent heart disease prediction system using data mining techniques”. Journal of Southwest Jiaotong University, 56(4).
Drożdż, K., Nabrdalik, K., Kwiendacz, H., Hendel, M., Olejarz, A., Tomasik, A., ... & Lip, G. Y. (2022). “Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease”: a machine learning approach. Cardiovascular Diabetology, 21(1), 240.
Boukhatem, C., Youssef, H. Y., & Nassif, A. B. (2022, February). “Heart disease prediction using machine learning”. In 2022 Advances in Science and Engineering Technology International Conferences (ASET) (pp. 1-6). IEEE.
Chandrasekhar, N., & Peddakrishna, S. (2023). “Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization”. Processes, 11(4), 1210.
Khan, A., Qureshi, M., Daniyal, M., & Tawiah, K. (2023).”A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction”. Health & Social Care in the Community, 2023.
Bizimana, P. C., Zhang, Z., Asim, M., El-Latif, A., & Ahmed, A. (2023). “An Effective Machine Learning-Based Model for an Early Heart Disease Prediction”. BioMed Research International, 2023.
Beyene, C., & Kamat, P. (2018). “Survey on prediction and analysis the occurrence of heart disease using data mining techniques”. International Journal of Pure and Applied Mathematics, 118(8), 165-174.
Fitriyani, N. L., Syafrudin, M., Alfian, G., & Rhee, J. (2020). “HDPM: an effective heart disease prediction model for a clinical decision support system”. IEEE Access, 8, 133034-133050.
Chowdhury, M. N. R., Ahmed, E., Siddik, M. A. D., & Zaman, A. U. (2021, April). “Heart disease prognosis using machine learning classification techniques”. In 2021 6th International Conference for Convergence in Technology (I2CT) (pp. 1-6). IEEE.
Kumari, A., & Mehta, A. K. (2021, August). “A novel approach for prediction of heart disease using machine learning algorithms”. In 2021 Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-5). IEEE.
Justo-Silva, R., Ferreira, A., & Flintsch, G. (2021). “Review on machine learning techniques for developing pavement performance prediction models”. Sustainability, 13(9), 5248.
Sruthi ER. Understanding random forest. https://www.analyticsvidhya. com/blog/2021/06/understanding-random-forest/, 2022.
Goel, R. (2021, July). “Heart disease prediction using various algorithms of machine learning”. In Proceedings of the International Conference on Innovative Computing & Communication (ICICC).
Fu, H., & Qi, K. (2022). “Evaluation Model of Teachers' Teaching Ability Based on Improved Random Forest with Grey Relation Projection”. Scientific Programming, 2022, 1-12.
Liu, G., Mao, S., & Kim, J. H. (2019). “A mature-tomato detection algorithm using machine learning and color analysis”. Sensors, 19(9), 2023.
Raizada, R. D., & Lee, Y. S. (2013). “Smoothness without smoothing: why Gaussian naive Bayes is not naive for multi-subject searchlight studies”. PloS one, 8(7), e69566.
Sánchez, A. S., Iglesias-Rodríguez, F. J., Fernández, P. R., & de Cos Juez, F. J. (2016). Applying the K-nearest neighbor technique to the classification of workers according to their risk of suffering musculoskeletal disorders. International Journal of Industrial Ergonomics, 52, 92-99.
Kilic, A. (2020). Artificial intelligence and machine learning in cardiovascular health care. The Annals of thoracic surgery, 109(5), 1323-1329.
Yousif, H., Al-saedi, K. H., & Al-Hassani, M. D. (2019). Mobile phishing websites detection and prevention using data mining techniques. iJIM, 13(10), 205.
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). “Artificial intelligence in precision cardiovascular medicine”. Journal of the American College of Cardiology, 69(21), 2657-2664.
Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). “Comparing different supervised machine learning algorithms for disease prediction”. BMC medical informatics and decision making, 19(1), 1-16.
Aparna, P., & Sharma, K. M. (2020, March). “Detection of A Fib and its Classification using SVM”. In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 116-120). IEEE.
Noble, W. S. (2006). “What is a support vector machine?”. Nature biotechnology, 24(12), 1565-1567.
Goel, R. (2021, July). “Heart disease prediction using various algorithms of machine learning”. In Proceedings of the International Conference on Innovative Computing & Communication (ICICC).
Fareed, M., & Al-Saedi, K. H. (2022, November). Proposal to enhance the information security system of the cloud using data mining techniques. In AIP Conference Proceedings (Vol. 2394, No. 1). AIP Publishing.
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Copyright (c) 2024 Raed Hassan Laftah, Karim Hashim Kraidi Al-Saedi
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