Cardio vascular disease prediction using machine learning web application
Keywords:
machine learning, heart disease prediction, feature selection, prediction model, classification algorithms, cardiovascular disease (CVD)Abstract
The Principal Target of this application to be create and make a strategy model that can forecast a patient's status of health based on a large amount of data. Coronary illness is the most common cause of mortality among humans. ML provides many classification techniques to divine the chance of a patient having HD based on the specified task. This project tries to recognize at-risk traits and classify data based on those aspects, allowing for exactprediction. To arrive at efficient findings, the dataset is first pre-handled and investigated against different calculations, with their accuracies evaluated. The efficient method is then employed in the back-end and connected with the front-end as a web application to predict whether or not a person has disease based on the input values. As a result, this web application would be the efficient need of the hour for obtaining precise health outcomes.
Downloads
References
Gharehchopogh, F. S., & Khalifelu, Z. A. (2011, July). Neural network application in diagnosis of patient: a case study. In International Conference on Computer Networks and Information Technology (pp. 245-249). IEEE.
Nag, P., Mondal, S., Ahmed, F., More, A., & Raihan, M. (2017, December). A simple acute myocardial infarction (Heart Attack) prediction system using clinical data and data mining techniques. In 2017 20th International Conference of Computer and Information Technology (ICCIT) (pp. 1-6). IEEE.
Raihan, M., Islam, M. M., Ghosh, P., Shaj, S. A., Chowdhury, M. R., Mondal, S., & More, A. (2018, December). A comprehensive analysis on risk prediction of acute coronary syndrome using machine learning approaches. In 2018 21st International Conference of Computer and Information Technology (ICCIT) (pp. 1-6). IEEE.
Sen, A.K., Patel, S.B., & Shukla, D.P. (2013). A Data Mining Technique for Prediction of Coronary Heart Disease Using Neuro-Fuzzy Integrated Approach Two Level.
Kunwar, V., Chandel, K., Sabitha, A. S., & Bansal, A. (2016, January). Chronic Kidney Disease analysis using data mining classification techniques. In 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence) (pp. 300-305). IEEE.
Ishtake, M. " Intelligent Heart Disease Prediction System Using Data Mining Techniques ".
Bhatla, N., & Jyoti, K. (2012). An Analysis of Heart Disease Prediction using Different Data Mining Techniques. International journal of engineering research and technology, 1.
Xie, J., Wu, R., Wang, H., Chen, H., Xu, X., Kong, Y., & Zhang, W. (2021). Prediction of cardiovascular diseases using weight learning based on density information. Neurocomputing, 452, 566-575.
Sultana, M., Haider, A., & Uddin, M. (2016). Analysis of data mining techniques for heart disease prediction. 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 1-5.
Rinartha, K., & Suryasa, W. (2017). Comparative study for better result on query suggestion of article searching with MySQL pattern matching and Jaccard similarity. In 2017 5th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-4). IEEE.
Rinartha, K., Suryasa, W., & Kartika, L. G. S. (2018). Comparative Analysis of String Similarity on Dynamic Query Suggestions. In 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS) (pp. 399-404). IEEE.
Kadir, Y. R., Syarif, S., Arsyad, M. A., Baso, Y. S., & Usman, A. N. (2021). Female’s reproductive health application design on the school teacher knowledge: an android-based learning media. International Journal of Health & Medical Sciences, 4(2), 189-195. https://doi.org/10.31295/ijhms.v4n2.1686
Published
How to Cite
Issue
Section
Copyright (c) 2022 International journal of health sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the International Journal of Health Sciences (IJHS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJHS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in IJHS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
This copyright notice applies to articles published in IJHS volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.
