IJARP Impact Factor(2018): 4.908

International Journal of Advanced Research and Publications!

Paper Details: Predicting Heart Diseases In Logistic Regression Of Machine Learning Algorithms By Python Jupyterlab

Volume 3 - Issue 8, August 2019 Edition
[Download Full Paper]

Author(s)
A. S. Thanuja Nishadi
Keywords
machine learning, logistic regression, classification algorithms, heart diseases
Abstract
Healthcare expenditures are overwhelming national and corporate budgets due to asymptomatic diseases including cardiovascular diseases. Therefore, there is an urgent need for early detection and treatment of such diseases. Machine learning is one of the trending technologies which used in many spheres around the world including healthcare industry for predicting diseases. The aim of this study is to identify the most significant predicators of heart diseases and predicting the overall risks by using logistic regression. Thus, binary logistic model which is one of the classification algorithms in machine learning is used in this study to identify thepredicators. Further, data analysis is carried out in Python using JupyterLab in order to validate the logistic regression.
References
[1]. Mozaffarian, D., Benjamin, E., Go, A., Arnett, D., Blaha, M.Cushman, M. et al. (2015). Heart Disease and Stroke Statistics—2015, Update. Circulation, 131(4). doi: 10.1161/cir.0000000000000152.

[2]. Das, S., Dey, A., Pal, A., & Roy, N. (2015). Applications of Artificial Intelligence in Machine Learning: Review and Prospect. International Journal of Computer Applications, 115(9), 31-41. doi: 10.5120/20182-2402

[3]. Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), 189. doi: 10.3390/su11010189

[4]. Strecht, Pedro & Cruz, Luís & Soares, Carlos & Moreira, João & Abreu, Rui. (2015). A Comparative Study of Classification and Regression Algorithms for Modelling Students' Academic Performance, , https://www.researchgate.net/publication/278030689_A_Comparative_Study_of_Classification_and_Regression_Algorithms_for_Modelling_Students'_Academic_Performance, viewed: 10th June 2019.

[5]. Sathya, R & Abraham, A (2013) International Journal of Advanced Research in Artificial Intelligence, Vol. 2, No. 2, 2013, http://ijarai.thesai.org/Downloads/IJARAI/Volume2No2/Paper_6-Comparison_of_Supervised_and_Unsupervised_Learning_Algorithms_for_Pattern_Classification.pdf, viewed: 10th June 2019.

[6]. CVA, K. (2017), https://www.medwinpublishers.com/JOBD/JOBD16000139.pdf. Journal of Orthopedics & Bone Disorders, 1(7). doi: 10.23880/jobd-16000139

[7]. Miguel-Hurtado, O., Guest, R., Stevenage, S., Neil, G., & Black, S. (2016). Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics. PLOS ONE, 11(11), e0165521. doi: 10.1371/journal.pone.0165521

[8]. Ng, A. Y. and Jordan, M. I. (2002). On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. In NIPS 14, pp. 841–848.

[9]. Peng, C., Lee, K., & Ingersoll, G. (2002). An Introduction to Logistic Regression Analysis and Reporting. The Journal of Educational Research, 96(1), 3-14. doi: 10.1080/00220670209598786

[10]. Park, H. (2019). An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to NursingDomain, https://pdfs.semanticscholar.org/3305/2b1d2363aee3ad290612109dcea0aed2a89e.pdf, viewed : 10th June 2019