Abstract
Heart conditions are classified as diverse illnesses with numerous subgroups. To make patient clinical treatment easier, early heart disease diagnosis and prognosis are crucial. Although much research has been done to predict the cause of heart disease. We have tried to build a heart disease detection system using machine learning algorithms. The basic goal of work is to detect whether a person is suffering from heart disease or not. We have also built a GUI using HTML and CSS for our front end and integrated both using flask for the back end. The user interface is available at https://github.com/SayanKumarBose/FinalYearProject.git.
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References
Gavhane, A., Kokkula, G., Pandya, I., Devadkar, K.: Prediction of heart disease using machine learning. In: Proceedings of the 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA); 2018; Coimbatore, India
Tan, K.C., Teoh, E.J., Yu, Q., Goh, K.C.: A hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst. Appl. 36(4), 8616–8630 (2009). https://doi.org/10.1016/j.eswa.2008.10.013
Rairikar, A., Kulkarni, V., Sabale, V., Kale, H., Lamgunde, A.: Heart disease prediction using data mining techniques. In: Proceedings of the 2017 International Conference on Intelligent Computing and Control (I2C2), Coimbatore, India (2017)
Krittanawong, C., et al.: Machine learning prediction in cardiovascular diseases: a meta-analysis. Sci. Rep. 10(1), 1–11 (2020)
Singh, A., Kumar, R.: Heart disease prediction using machine learning algorithms. In: 2020 International Conference on Electrical and Electronics Engineering (ICE3), pp. 452–457. IEEE, February 2020
Nahar, J., Imam, T., Tickle, K.S., Chen, Y.-P.P.: Computational intelligence for heart disease diagnosis: a medical knowledge driven approach. Expert Syst. Appl. 40(1), 96–104 (2013). https://doi.org/10.1016/j.eswa.2012.07.032
Dey, L., Chakraborty, S., Biswas, A., Bose, B., Tiwari, S.: Sentiment analysis of review datasets using naive bayes and k-nn classifier. arXiv preprint arXiv:1610.09982 (2016)
Chakraborty, S., Kumar, S., Paul, S., Kairi, A.: A study of product trend analysis of review datasets using Naive Bayes, K-NN and SVM classifiers. Int. J. Adv. Eng. Manag. 2(9), 204–213 (2017)
Anbuselvan, P.: Heart disease prediction using machine learning techniques. Int. J. Eng. Res. Technol 9, 515–518 (2020)
Rajdhan, A., Agarwal, A., Sai, M., Ravi, D., Ghuli, P.: Heart disease prediction using machine learning. Int. J. Res. Technol. 9(04), 659–662 (2020)
Rindhe, B.U., Ahire, N., Patil, R., Gagare, S., Darade, M.: . Heart disease prediction using machine learning. Heart Disease 5(1)
Srivenkatesh, M.: Prediction of cardiovascular disease using machine learning algorithms. Int. J. Eng. Adv. Technol 9(3), 2404–2414 (2020)
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Bose, S.K., Ghosh, S., Das, S., Bhowmick, S., Talukdar, A., Dey, L. (2024). A GUI-Based Approach to Predict Heart Disease Using Machine Learning Algorithms and Flask API. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1955. Springer, Cham. https://doi.org/10.1007/978-3-031-48876-4_23
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