CardioInsights: Heart Disease Prediction Using Machine Learning Algorithms
Keywords:
Machine learning, Logistic Regression, Random Forest, Heart disease prediction, Medical data analyticsAbstract
In recent years, there has been a critical surge within the predominance of heart illness, and it is related with a increased mortality rate. Our point is to survey the accuracy of wellbeing markers in foreseeing heart illness when compared to the comprehensive set of pointers measured by healthcare experts and the dataset. This handle will include the application of five machine learning models: Coordinations Relapse, K Closest Neighbors, Back Vector Show, Choice tree, and Arbitrary Woodland. Especially Heart Diseases have become a major issue these days. WHO report that over [1]11 million deaths are caused each year worldwide due to heart related complications. Using this we can understand that the condition is very serious. There is an urgent need for some technological solutions which can help in educating people and early diagnosis of these disease. This requires a string infrastructure including a large enough work force, which is a slow process. Sudden increase in the number of patients depict the need for scalability in our medical system. While work force may be limited, we can look for algorithmic solutions for screening and diagnosis stages, due to their correlation with Our project "CardioInsights" is a system that uses predictive capability of machine learning models based on similar data points collected in past. We are going to use the previous medical report data to predict the risk of disease. Using our system patient can estimate chance of being suffer from heart disease. We are trying to provide the user-friendly platform to user that can be access remotely to screening facilities that will predict the risk of heart disease and that will eliminate the load on the medical system.
References
E. E. Tripoliti, T. G. Papadopoulos, G. S. Karanasiou, K. K. Naka, and D. I. Fotiadis, “Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques,” Computational and Structural Biotechnology Journal, vol. 15. Elsevier B.V., pp. 26–47, 2017. doi: 10.1016/j.csbj.2016.11.001.
A. Javeed, S. Zhou, L. Yongjian, I. Qasim, A. Noor, and R. Nour, “An Intelligent Learning System Based on Random Search Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection,” IEEE Access, vol. 7, pp. 180235–180243, 2019, doi: 10.1109/ACCESS.2019.2952107.
K. Vembandasamyp, R. R. Sasipriyap, and E. Deepap, “Heart Diseases Detection Using Naive Bayes Algorithm,” 2015. [Online]. Available: www.ijiset.com
Z. Masetic and A. Subasi, “Congestive heart failure detection using random forest classifier,” Comput Methods Programs Biomed, vol. 130, pp. 54–64, Jul. 2016, doi: 10.1016/j.cmpb.2016.03.020.
“Sushmita Roy Tithi, AfifaAktar,FahimulAleem,AmitabhaChakrabarty.”
B. Jin, C. Che, Z. Liu, S. Zhang, X. Yin, and X. Wei, “Predicting the Risk of Heart Failure with EHR Sequential Data Modeling,” IEEE Access, vol. 6, pp. 9256–9261, Jan. 2018, doi: 10.1109/ACCESS.2017.2789324.
Y. Muhammad, M. Tahir, M. Hayat, and K. T. Chong, “Early and accurate detection and diagnosis of heart disease using intelligent computational model,” Sci Rep, vol. 10, no. 1, Dec. 2020, doi: 10.1038/s41598-020-76635-9.
H. Sun and J. Pan, “Heart Disease Prediction Using Machine Learning Algorithms with Self-Measurable Physical Condition Indicators,” Journal of Data Analysis and Information Processing, vol. 11, no. 01, pp. 1–10, 2023, doi: 10.4236/jdaip.2023.111001.
S. Nashif, Md. R. Raihan, Md. R. Islam, and M. H. Imam, “Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System,” World Journal of Engineering and Technology, vol. 06, no. 04, pp. 854–873, 2018, doi: 10.4236/wjet.2018.64057.
D. Sandhya, “Heart Disease Prediction using Machine Learning Algorithms,” International Research Journal of Engineering and Technology, 2022, [Online]. Available: www.irjet.net
R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, and P. Singh, “Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning,” ComputIntellNeurosci, vol. 2021, 2021, doi: 10.1155/2021/8387680.
R. Indrakumari, T. Poongodi, and S. R. Jena, “Heart Disease Prediction using Exploratory Data Analysis,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 130–139. doi: 10.1016/j.procs.2020.06.017.
M. Kavitha, G. Gnaneswar, R. Dinesh, Y. R. Sai, and R. S. Suraj, “Heart Disease Prediction using Hybrid machine Learning Model,” in Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021, Institute of Electrical and Electronics Engineers Inc., Jan. 2021, pp. 1329–1333. doi: 10.1109/ICICT50816.2021.9358597.
S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, pp. 81542–81554, 2019, doi: 10.1109/ACCESS.2019.2923707.
Purushottam, K. Saxena, and R. Sharma, “Efficient Heart Disease Prediction System,” in Procedia Computer Science, Elsevier B.V., 2016, pp. 962–969. doi: 10.1016/j.procs.2016.05.288.
H. Jindal, S. Agrawal, R. Khera, R. Jain, and P. Nagrath, “Heart disease prediction using machine learning algorithms,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing Ltd, Jan. 2021. doi: 10.1088/1757-899X/1022/1/012072.
Z. Arabasadi, R. Alizadehsani, M. Roshanzamir, H. Moosaei, and A. A. Yarifard, “Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm,” Comput Methods Programs Biomed, vol. 141, pp. 19–26, Apr. 2017, doi: 10.1016/j.cmpb.2017.01.004.
J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, “Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare,” IEEE Access, vol. 8, pp. 107562–107582, 2020, doi: 10.1109/ACCESS.2020.3001149.
SCAD College of Engineering and Technology and Institute of Electrical and Electronics Engineers, Proceedings of the International Conference on Trends in Electronics and Informatics (ICOEI 2019) : 23-25, April 2019.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Applied Optics
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The CC Attribution-NonCommercial 4.0 License allows sharing and adapting the work, provided the creator is credited and the work is not used commercially. Modifications must be indicated, and derivative works under the same license are allowed.
