Open Access Open Access  Restricted Access Subscription Access

CARDIOM HEALTH PREDICTION

Bindurani HA ., Disha Devadiga, Gracy J, Sirisha ., Deepak NR, Shruthi B

Abstract


Heart Disease remains a leading cause of world wide requiring early diagnosis to improve patient outcomes.This study proposes a machine- learning based on heart disease,utilizing a dataset of 270 patients with features like Age, Sex, Chestpain, Blood Pressure, Cholestrol, FBS over 120, EKG Results, Max HR, Exercise angina, ST Depression, Slope of ST, Number of vessels fluro, Thallium, Heart Disease. In this project we mainly take a look on the dataset which we have been uploded in this prediction project .The data set that consist of the details about the many patients. The data which are present in the dataset is the details about the patients so it is easy to analyze the disease of the peoples by their health condition , here for writing the code we import the libraries such as pandas , mathplotlib NumPy etc and then we upload the dataset to the program using csv file . Heart disease takes palce in the machine learing process in which the output of the prediction is formed by the visualiztion graph , in this heart disease prediction project the visualization can be done by the different types of graph such as Bar Graph , distribution plot , ROC curve , confusion matrix heatmap , stackes , scatter plot for feature analysis.By using this we come to know that 80% of patients are not having heart disease and 20% of patients are having the heart disease.

Full Text:

PDF

References


• Libby, P., & Loscalzo, J. (2011). Braunwald's Heart Disease: A Textbook of Cardiovascular Medicine. Elsevier Health Sciences.

This book provides a comprehensive overview of heart disease, including diagnostic and prognostic factors that may inform feature selection in your model[1]

• Tandon, D., & Tandon, R. (2018). "Machine Learning Approaches for Predicting Cardiovascular Disease," Journal of Health Informatics, 34(2), 125-134.

This paper gives various machine learning methods applied to heart disease prediction, discussing performance metrics and techniques that may serve as benchmarks for your project[2]

• Kannel, W. B. (2000). "Risk stratification in hypertension: New insights from the Framingham Study," American Journal of Hypertension, 13(1), 3S-10S.

The Framingham study has significantly influenced cardiovascular Risk assessment. This reference provides foundational insight into the risk factors often used in predictive models[3]

• Deepak, N.R., Balaji, S. (2016). Uplink Channel Performance and Implementation of Software for Image Communication in 4G Network. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives and Application in Intelligent Systems. CSOC 2016. Advances in Intelligent Systems and Computing, vol 465. Springer, Cham. https://doi.org/10.1007/978-3-319-33622- 0_10[4]

• Simran Pal R and Deepak N R, “Evaluation on Mitigating Cyber Attacks and Securing Sensitive Information with the Adaptive Secure Metaverse Guard (ASMG) Algorithm

Using Decentralized Security”, Journal of Computational Analysis and Applications (JoCAAA), vol. 33, no. 2, pp. 656–667, Sep. 2024[5]

• B, Omprakash & Metan, Jyoti & Konar, Anisha & Patil, Kavitha & KK, Chiranthan. (2024). Unravelling Malware Using Co-Existence Of Features. 1-6. 10.1109/ICAIT61638.2024.10690795[6]

• Rezni S and Deepak N R, “Challenges and Innovations in Routing for Flying Ad Hoc Networks: A Survey of Current Protocols”, Journal of Computational Analysis and Applications (JoCAAA), vol. 33, no. 2, pp. 64–74, Sep.

[7]

• N. R. Deepak and S. Balaji, "Performance analysis of MIMO-based transmission techniques for image quality in 4G wireless network," 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2015, pp. 1-5, doi: 10.1109/ICCIC.2015.7435774[8]

• N R, Deepak & Sriramulu, Balaji. (2015). A Review of Techniques used in EPS and 4G-LTE in Mobility Schemes. International Journal of Computer Applications.

30-38. 10.5120/19219-1018[9]

• Patil, Kavitha S et al. “Hybrid and Adaptive Cryptographic-based secure authentication approach in Io T based applications using hybrid encryption.” Pervasive Mob. Comput. 82 (2022): 101552[10]


Refbacks

  • There are currently no refbacks.