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Ragini, Kumari
- Machine Learning Based Heart Disease Prediction Model with GUI Interface
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Authors
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1 Student, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
1 Student, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 261-276Abstract
One of the top causes of death around the globe is a heart attack. According to current statistics, one person dies from heart disease every minute, making it one of the primary problems in everyday modern life. The ability to predict the onset of illness at an early stage is extremely difficult nowadays. When used in the healthcare industry, machine learning has the potential to accurately and quickly diagnose diseases. The circumstances under which heart disease may arise are estimated in this study. Medical parameters are characteristics of the datasets utilized. The datasets are analyzed using the Random Forest Algorithm, a machine learning algorithm, in Python. This method makes use of historical patient data from the past to forecast future ones at an early stage, saving lives. In this study, a trustworthy system for predicting heart disease is put into place utilizing a powerful machine learning algorithm called the Random Forest method. This reads a CSV file containing patient record data. After gaining access to the dataset, the procedure is carried out, and a useful heart attack level is generated. The suggested system's benefits include High success rates are attained, along with excellent performance and accuracy rates, flexibility, and adaptability.Keywords
Machine Learning, Artificial Intelligence, Heart Disease.Full Text
References
- Asif Newaz, Nadim Ahmed, FarhanShahriyarHaq,"Survival prediction of heart failure patients using machine learning techniques",Informatics in Medicine Unlocked,Volume 26,2021,100772,ISSN 2352-9148,https://doi.org/10.1016/j.imu.2021.100772.
- ]COŞKUN, Cevdet, and Fatma KUNCA, "EVALUATION OF PERFORMANCE OF CLASSIFICATION ALGORITHMS IN PREDICTION OF HEART FAILURE DISEASE", KahramanmaraşSütçü İmam ÜniversitesiMühendislikBilimleriDergisi 25.4 (2022): 622-632.
- Alotaibi, Fahd Saleh, "Implementation of machine learning model to predict heart failure disease", International Journal of Advanced Computer Science and Applications 10.6 (2019).
- Ramesh, T. R., et al, "Predictive analysis of heart diseases with machine learning approaches", Malaysian Journal of Computer Science (2022): 132-148.
- Garg, Apurv, Bhartendu Sharma, and Rijwan Khan. "Heart disease prediction using machine learning techniques", IOP Conference Series: Materials Science and Engineering. Vol. 1022. No. 1. IOP Publishing, 2021.
- Sharma, Sumit, and Mahesh Parmar. "Heart diseases prediction using deep learning neural network model", International Journal of Innovative Technology and Exploring Engineering (IJITEE) 9.3 (2020): 2244-2248.
- Abdellatif, Abdallah, et al, "Improving the heart disease detection and patients’ survival using supervised infinite feature selection and improved weighted random forest", IEEE Access 10 (2022): 67363-67372.
- Jabbar, M. A., B. L. Deekshatulu, and Priti Chandra, "Intelligent heart disease prediction system using random forest and evolutionary approach", Journal of network and innovative computing 4.2016 (2016): 175-184.
- Bonaccorso, Giuseppe,” Machine learning algorithms”, Packt Publishing Ltd, 2017.
- Ray, Susmita,"A quick review of machine learning algorithms", 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon). IEEE, 2019.
- Singh, Amanpreet, Narina Thakur, and AakankshaSharma,"A review of supervised machine learning algorithms", 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). Ieee, 2016.
- Mohammed, Mohssen, Muhammad Badruddin Khan, and EihabBashier Mohammed Bashier,”Machine learning: algorithms and applications”Crc Press, 2016.
- Fatima, Meherwar, and MarufPasha,"Survey of machine learning algorithms for disease diagnostic", Journal of Intelligent Learning Systems and Applications 9.01 (2017): 1.
- Osisanwo, F. Y., et al. ,"Supervised machine learning algorithms: classification an dcomparison.", International Journal of Computer Trends and Technology (IJCTT) 48.3 (2017): 128-138.
- Guo, Gongde, et al. "KNN model-based approach in classification." On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy, November 3-7, 2003. Proceedings. Springer Berlin Heidelberg, 2003.
- Guo, Gongde, et al. "KNN model-based approach in classification." On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy, November 3-7, 2003. Proceedings. Springer Berlin Heidelberg, 2003.
- Zhang, Shichao, et al. "Learning k for knn classification." ACM Transactions on Intelligent Systems and Technology (TIST) 8.3 (2017): 1-19.
- Zhang, H., Berg, A.C., Maire, M. and Malik, J., 2006, June. SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) (Vol. 2, pp. 2126-2136). IEEE.
- Wang, Haifeng, and Dejin Hu. "Comparison of SVM and LS-SVM for regression." 2005 International conference on neural networks and brain. Vol. 1. IEEE, 2005.
- Schuldt, Christian, Ivan Laptev, and Barbara Caputo. "Recognizing human actions: a local SVM approach." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., vol. 3, pp. 32-36. IEEE, 2004.
- Vishwanathan SV, Murty MN. SSVM: a simple SVM algorithm. InProceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No. 02CH37290) 2002 May 12 (Vol. 3, pp. 2393-2398). IEEE.
- Cherkassky, Vladimir, and Yunqian Ma. "Practical selection of SVM parameters and noise estimation for SVM regression." Neural networks 17, no. 1 (2004): 113-126.
- Mahesh, Batta. "Machine learning algorithms-a review." International Journal of Science and Research (IJSR).[Internet] 9 (2020): 381-386.
- Bi, Q., Goodman, K. E., Kaminsky, J., &Lessler, J. (2019). What is machine learning? A primer for the epidemiologist. American journal of epidemiology, 188(12), 2222-2239.
- Mair C, Kadoda G, Lefley M, Phalp K, Schofield C, Shepperd M, Webster S. An investigation of machine learning based prediction systems. Journal of systems and software. 2000 Jul 15;53(1):23-9.
- Mackenzie, Adrian. "The production of prediction: What does machine learning want?." European Journal of Cultural Studies 18.4-5 (2015): 429-445.