Heart Disease Prediction and Analysis using various Machine Learning Algorithms

References

[1] . Harshit Jindal, Sarthak Agrawal, Rishabh Khera, Rachna Jain and Preeti Nagrath, “Heart disease prediction using machine learning algorithms”, ICCRDA 2020, IOP Conf. Series: Materials Science and Engineering, 1022 (2021) 012072 IOP Publishing 899X/1022/1/012072

[2] . Pranav Motarwar, Ankita Duraphe, G Suganya, M Premalatha, “Cognitive Approach for Heart Disease Prediction using Machine Learning”, 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), IEEE, 2020, 10.1109/ic-ETITE47903.2020.242

[3] . Vijeta Sharma; Shrinkhala Yadav; Manjari Gupta, “Heart Disease Prediction using Machine Learning Techniques”, 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), IEEE, 18-19 Dec. 2020,

[4] . Atharv Nikam; Sanket Bhandari; Aditya Mhaske; Shamla Mantri, “Cardiovascular Disease Prediction Using Machine Learning Models” 2020 IEEE Pune Section International Conference (PuneCon), IEEE, 16-18 Dec. 2020,

[5] . Senthilkumar Mohan, Chandrasegar Thirumalai, Gautam Srivastava, “Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques”, IEEE Access, Special Section On Smart Caching, Communications, Computing And Cybersecurity For Information-Centric Internet Of Things, 10.1109/ACCESS.2019.2923707

[6] . Dinesh Kumar G, Santhosh Kumar D, Arumugaraj K, Mareeswari V, “Prediction of Cardiovascular Disease Using Machine Learning Algorithms”, Proceeding of 2018 IEEE International Conference on Current Trends toward Converging Technologies, Coimbatore, India

[7] . Aditi Gavhane, Gouthami Kokkula, Isha Pandya, Prof. Kailas Devadkar, “Prediction of Heart Disease Using Machine Learning”, Proceedings of the 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA 2018), IEEE Conference, IEEE Xplore ISBN:978-1-5386-0965-1

[8] . Zhang, Shuai, Y.-L. S. A. (2017), ‘Deep learning based recommender system: a survey and new perspectives’, Journal of ACM Computing Surveys 1(1), 1–35.

[9] . AminKhatami, AbbasKhosravi, C. L. (2017), ‘Medical image analysis using wavelet transform and deep belief networks’, Journal of Expert Systems With Applications 3(4), 190–198.

[10] . AshwiniShetty, Naik, C. (2016), ‘Different data mining approaches for predicting heart disease’, International journal of innovative research in science, engineering and technology 3(2), 277– 281.

[11] . Aydin, S. (2016), ‘Comparison and evaluation data mining techniques in the diagnosis of heart disease’, Indian journal of science and technology 6(1), 420–423.

[12] . Bayasi, N. and Tekeste (2016), ‘Low-power ECG-based processor for predicting ventricular arrhythmia’, Journal of IEEE transactions on very large scale integration systems 24(5), 1962– 1974.

[13] . Berikol, B. and Yildiz (2016), ‘Diagnosis of acute coronary syndrome with a support vector machine’, Journal of Medical System 40(4), 11– 18.

[14] . Zhiyong Wang, Xinfeng Liu, J. G. (2016), ‘Identification of metabolic biomarkers in patients with type-2 diabetic coronary heart diseases based on metabolomic approach’, 6(30), 435–439.

[15] . Singh, M. and Martins (2016), ‘Building a cardiovascular disease predictive model using structural equation model and fuzzy cognitive map’, Journal of fuzzy system 02(6), 1377–1382.

[16] . S.Prabhavathi (2016), ‘Analysis and prediction of various heart diseases using DNFS techniques’, International journal of innovations in scientific and engineering research 2(7), 678– 684.

[17] . Sali, R. and Shavandi, M. (2016), ‘A clinical decision support system based on support vector machine and binary particle swarm optimisation for Cardiovascular disease diagnosis’, International Journal of Data mining and Bio- informatics 15(1), 312–327.

[18] . Ghadge, Prajakta, K. (2016), ‘Intelligent heart attack prediction system using big data’, International journal of recent research in mathematics computer science and information technology 2(2), 73–77.

[19] . G.Purusothaman and K.Krishnakumari (2015), ‘A survey of data mining techniques on risk prediction: heart disease’, Indian journal of science and technology 8(5), 643–651.

[20] . Richter, A., Listing, J., Schneider, M., Klopsch, T., Kapelle, A., Kaufmann, J., Zink, A. and Strangfeld, A. (2015), ‘Impact of treatment with biologic dmards on the risk of sepsis or mortality after serious infection in patients with rheumatoid arthritis’, Annals of the rheumatic diseases pp. 147–153.

[21] . Sairabi, Mujawar, D. (2015), ‘Prediction of Hear t Disease using Modified K-means and by using Naive Bayes’, International Journal of Innovative Research in Computer and Communication Engineering 3.

[22] . Vafaie, Ataei, M. (2014), ‘Heart diseases prediction based on ECG signals classification using a genetic-fuzzy system’, Journal of biomedical signal processing and control 14(5), 291–296.

[23] . Wang (2014), ‘Study on qi-deficiency syndrome identification modes of Coronary heart disease based on metabolomic biomarkers’, Journal of evidence-based complementary and alternative medicine 24(16), 192–198.

[24] . Yang, X., Li, M., Zhang, Y. and Ning, J. (2014), ‘Cost-sensitive naive bayes classification of uncertain data’, Journal of Scientific World 9(8), 1897–1904.

[25] . Chandna, D. (2014), ‘Diagnosis of heart disease using data mining algorithm’, International journal of computer science and information technologies 5(2), 1678–1680.

@article{1719790,
      author = {Soumya Tripathi, Tanya Verma, Sunny, Harsh},
      title = {Heart Disease Prediction and Analysis using various Machine Learning Algorithms},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {9},
      pages = {567-572},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719790.pdf},
      abstract = {Despite numerous advancements in modern medicine, cardiovascular diseases still rank among the top mortality factors all around the world, with 18 million annual deaths caused by heart diseases according to the World Health Organization estimates. Identification of individuals with elevated risk of heart diseases is important in order to reduce the mortality rate especially in developing countries with limited access to medical facilities. Thanks to recent progress made in Artificial Intelligence and Machine Learning, it became possible to create intelligent diagnostic systems that can help doctors diagnose patients at an early stage. This paper aims at evaluating the performance of five supervised machine learning algorithms for heart disease prediction, namely, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and K-Nearest Neighbors. Based on experimental evaluation, the Random Forest algorithm shows the best performance providing an accuracy of 90.16% while logistic regression shows accuracy of 85.25%, and the KNN classifier shows the lowest accuracy level of 67.21%. In addition to classification accuracy, the precision and recall values are also estimated based on the confusion matrix. The achieved results suggest that ensembles of learning algorithms provide better predictions while maintaining appropriate level of generalization.},
      keywords = {Random Forest, Decision Tree, SVM, Machine Learning, Heart Disease, KNN, Logistic Regression},
      month = {March},
      doi = {https://doi.org/10.64388/IREV6I9-1719790}
  }