Heart Disease Prediction Using Logistic Regression

Main Article Content

Kavya S. M.
Prathanya Sree C.
Deepasindhu M.
Nowshika B.
Shijitha R.

Abstract

Myocardial Infarction and Brain attacks are responsible for the fatalities of individuals from cardiovascular diseases (CVDs), and especially the deaths occur before age 70. 17.9 million people are thought to pass away from CVDs annually. Accurate monitoring for each patient individually is not always possible, and clinicians cannot consult with patients every 24 hours due to the additional time and knowledge required. Using the patient's various cardiac characteristics and the machine learning approach of logistic regression on a publicly accessible dataset from Kaggle, we developed and examined models for predicting heart disease in this research. The main objective is to ascertain of acquiring coronary heart disease (CHD) upto 10 years of health risk. More than 4,000 records, 15 attributes, and patient data are included in the collection. To forecast outcomes, it makes predictions about a dependent variable based on one or more sets of independent variables. Both binary classification and multi-class classification can use it. This study aims to establish the most significant heart disease risk factors and estimate the overall risk using logistic regression.

Article Details

How to Cite
S. M., K. ., Sree C., P. ., M., D. ., B., N. ., & R., S. . (2023). Heart Disease Prediction Using Logistic Regression. Journal of Coastal Life Medicine, 11(1), 573–579. Retrieved from https://www.jclmm.com/index.php/journal/article/view/380
Section
Articles

References

Divyansh Khanna, Rohan Sahu, Veeky Baths, and Bharat Deshpande Comparative Study of Classification Techniques (SVM, Logistic Regression and Neural Networks) to Predict the Prevalence of Heart Disease, International Journal of Machine Learning and Computing, Vol. 5, No. 5, October 2015.

N Satyanandam, Dr. Ch Satyanarayana,Heart Disease Prediction using predictive optimization techniques, International Journal of image, graphics and signal processing, Vol. 11, No. 9, September 2019.

Paria Soleimani, ArezooNeshati, Applying the regression technique for the prediction of acute heart attack, World Academy of Science Engineering and Technology, International Journal of Biomedical Biological Engineering, Vol. 9, No. 11, 2015.

Dr T Lalitha, future prediction of heart disease through exploratory analysis of data, smart green connected societies, vol. 1 no. 01, 2021

Abhijna Bhat, Pragathi, Pranamya M S, Smitha,Prediction of Heart Disease Using Logistic Regression, International Research Journal of Engineering and Technology, Vol 07, Issue: 06June, 2020.

AnimeshHazra,Subrata Kumar Mandal, Amit Gupta, Arkomita Mukherjee, Asmita Mukherjee, Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review, Advances in Computational Sciences and Technology Vol 10, Number 7, 2017.

A, S ThanujaNishadipapers, Predicting Heart Diseases in Logistic Regression of Machine Learning Algorithms by PythinJupyterlab, International Journal of Advanced Research and Publications,Vol3, Number 8, 2019.

Dinesh Kumar G, Prediction of cardiovascular disease using machine learning algorithms, preceding of 2018 IEEE International Conference on Current Trends toward Converging Technologies, Coimbatore,India, 2018.

Purushottam, Efficient heart disease prediction system using decision tree, 06 July 2015.