Open Access Open Access  Restricted Access Subscription Access

Leveraging Logistic Regression for Heart Disease Prediction: An Integrative Approach

Nikhat Parween, Jui Dey, Soumitra Mondal, Yeash Jain, Pritam Routh, Dr. Anindita Mukherjee.

Abstract


Cardiovascular diseases remain a significant public health problem that requires the development of

accurate predictive models to facilitate early diagnosis and intervention. Machine learning (ML) techno logy provides a promising way to analyze complex medical data and predict a person's risk of heart disease. This article provides detailed information on cardiovascular disease prediction based on logistic regression, a widely used machine learning algorithm known for its simplicity, interpretability, and performance in binary division of labor. The system combines clinical and demographic characteristics, including age, gender, blood pressure, cholesterol levels, rapid blood glucose levels, and electrocardiogram (ECG) to create predictive models for cardiovascular risk assessment. Use data prioritization techniques to resolve missing values, ensure consistent features, and resolve class inconsistencies in data. In addition, feature engineering methods are used to extract new information and increase the discrimination power of the model.


Full Text:

PDF

References


Prasad, R., Anjali, P., Adil, S., & Deepa, N. (2019). Heart disease prediction using logistic regression algorithm using machine learning. International Journal of Engineering and Advanced Technology, 8(3 Special Issue), 659–662.

Nishadi, A. S. T. (2019). Predicting Heart Diseases in Logistic Regression of Machine Learning Algorithms by Python Jupyterlab. 3(8), 69–74.

Dharani, M. M. K., & Poovitha, C. (2017). a Data Mining Model to Predict the Risk of Heart Disease Using Multinomial Logistic Regression (Mlr). 8(7), 66–70.

Amin, M. S., Chiam, Y. K., & Varathan, K. D. (2019). Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics, 36, 82–93.

Varun, S. A., Mounika, G., Sahoo, P. K., & Eswaran, K. (2019). Efficient System for Heart Disease Prediction by applying Logistic Regression. 8491, 13–16.

Peng, C. Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. Journal of Educational Research, 96(1), 3–14.

Dutta, A., Bandopadhyay, G., & Sengupta, S. (2012). Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression. International Journal of Business and Information, 7(1), 105–136.

Diaz, A. A., Tomba, E., Lennarson, R., Richard, R., Bagajewicz, M. J., & Harrison,

R. G. (2010). Prediction of Protein Solubility in Escherichia coli Using Logistic Regression. 105(2), 374–383.

Banerjee, I., Banerjee, I., Roy, B., & Sathian, B. (2013). Application of Binary Regression Analysis in the Prescription Pattern of Antidepressants. Medical Science, 1(1), 19.

Bernholt, T., Nunkesser, R., & Schettlinger, K. (2007). Computing the least quartile difference estimator in the plane. Computational Statistics and Data Analysis, 52(2), 763–772. Zulkiflee et al., Enhanced Knowledge in Sciences and Technology Vol. 1 No. 2 (2021) p. 177-184 184 [11] Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the median absolute deviation.


Refbacks

  • There are currently no refbacks.