Role of Exploratory Analytics and Visualization in Heart Disease Prediction

Role of Exploratory Analytics and Visualization in Heart Disease Prediction

© 2022 by IJETT Journal
Volume-70 Issue-1
Year of Publication : 2022
Authors : Lijetha.C. Jaffrin, Dr.J. Visumathi, Dr.G.Umarani Srikanth
DOI :  10.14445/22315381/IJETT-V70I1P203

How to Cite?

Lijetha.C. Jaffrin, Dr.J. Visumathi, Dr.G.Umarani Srikanth, "Role of Exploratory Analytics and Visualization in Heart Disease Prediction," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 22-28, 2022. Crossref,

Exploratory Data Analysis, Visualization, Spyder IDE, python, Seaborn.

Data Analysis is carried out to discover useful knowledge from the dataset and to drive quick and better decisions. It is also used to increase the efficiency of the work. Exploratory Data analysis is the first phase in Data Analysis. It is a method to understand the data and summarize the main features in the dataset by analyzing the data. It is also used for the visual representation of data. Visualization includes line plot, subplot, pair plot, violin plot, joint plot, swarm plot, Histograms, Box plot, Scatter plot. In this paper, Exploratory Data Analysis is done using python and implemented in Spyder IDE. Univariate analysis, bivariate analysis, multivariate analysis, and dimensionality reduction have been done on variables in the heart dataset. Different types of graphs have been plotted using the python Seaborn library to analyze the heart dataset. The primary objective is to get a more explained sight for which sort of traits might be a more critical sign of approaching heart disease.

[1] Kabita Sahoo, Abhaya Kumar Samal, Jitendra Pramanik, Subhendu Kumar Pani, Exploratory Data Analysis using Python, International Journal of Innovative Technology and Exploring Engineering(IJITEE) ISSN: 2278-3075, 8(12) (2019).
[2] Tejas Nanaware, Prashant Mahajan, Ravi Chandak, Pratik Deshpande, Prof. Mahendra Patil, Exploratory Data Analysis Using Dimension Reduction, Exploratory Data Analysis Using Dimension Reduction, IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719, 2 81-84
[3] John T. Behrens, Principles and Procedures of Exploratory Data Analysis, Psychological Methods, 2(2) (1997)131-160.
[4] Aindrila Ghosh, Mona Nashaat, James Miller, Shaikh Quader, and Chad Marston, A Comprehensive Review of Tools for Exploratory Analysis of Tabular Industrial Datasets, Visual Informatics, 2(4) (2018) 235-253
[5] R. Indrakumari, T. Poongodi and Soumya Ranjan Jena Heart Disease Prediction using Exploratory Data Analysis, Procedia Computer Science, 173 (2020) 130–139.
[7] https://rstudio-pubsstatic. 8fe.html
[8] Rony Chowdhury Ripan, Iqbal H. Sarker, Md. Hasan Furhad, Md Musfique Anwar, and Mohammed Moshiul Hoque, An Effective Heart Disease Prediction Model based on Machine Learning Techniques, Preprints 2020, 2020110744 (doi: 10.20944/preprints202011. 0744.v1)
[9] Shah Apeksha, Ahirrao Swati, Pandya Sharnil, Kotecha Ketan, Rathod Suresh, Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk, Frontiers in Public Health,
[10] Imran Chowdhury Dipto, Tanzila Islam, H M Mostafizur Rahman, Md Ashiqur Rahman, Comparison of Different Machine Learning Algorithms for the Prediction of Coronary Artery Disease, Journal of Data Analysis and Information Processing, 8 (2020) 41-68, DOI: 10.4236/jdaip.2020.82003
[11] Jinglin Peng, Weiyuan Wu, Brandon Lockhart, Song Bian, Jing Nathan Yan, Linghao Xu, Zhixuan Chi, Jeffrey M. Rzeszotarski, Jiannan Wang, DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python, arXiv:2104.00841v2 [cs.DB] 10 Apr, (2021).
[12] Sumaya Habib, Maisha Binte Moin, Sujana Aziz, Kalyan Banik, Hossain Arif, Heart Failure Risk Prediction and Medicine Recommendation using Exploratory Data Analysis”, 1st International Conference on Advances in Science, Engineering and Robotics Technology , (2019). 978-1-7281-3445-1, IEEE.
[13] Ching-seh (Mike) Wu, Mustafa Badshah, Vishwa Bhagwat, Heart Disease Prediction Using Data Mining Techniques, In Proceedings of 2019 2nd International Conference on Data Science and Information Technology (DSIT’19). Seoul, Korea, (2019) 5 pages.
[14] Dr T Lalitha, & Didwania, R., Future Prediction of Heart Disease through Exploratory Analysis of Data. SPAST Abstracts, 1(01) (2021).
[15] H. Agrawal, J. Chandiwala, S. Agrawal and Y. Goyal, "Heart Failure Prediction using Machine Learning with Exploratory Data Analysis, 2021 International Conference on Intelligent Technologies (CONIT), (2021) 1-6, doi: 10.1109/CONIT51480.2021.9498561.
[16] Dr. P.K.A. Chitra, Dr. P. Udaykumar, Exploratory Analysis to Predict Heart Disease Occurrence through machine Learning Approaches, International Journal of Advanced Science and Technology, 29(9) (2020) 2702-2709.
[17] Akella, Aravind, and Sudheer Akella, Machine learning algorithms for predicting coronary artery disease: efforts toward an open-source solution, Future science OA , FSO698. 29 Mar., 7(6) (2021) doi:10.2144/fsoa-2020-0206.
[18] Owk Mrudula, A.Mary Sowjanya, Understanding Clinical Data using Exploratory Analysis, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, 8(5) (2020).
[19] Dhai Eddine Salhi, Abdelkamel Tari, and M-Tahar Kechadi, Using Machine Learning for Heart Disease Prediction, In book: Advances in Computing Systems and Applications (2021) 70-81. February 2021.DOI:10.1007/978-3-030-69418-0_7.
[20] Rishabh Magar, Rohan Memane, Suraj Raut, Prof. V. S. Rupnar, Heart Disease Prediction Using Machine Learning, Journal of Emerging Technologies and Innovative Research (JETIR), 7(6) (2020).
[21] M. Thangamani, R. Vijayalakshmi, M. Ganthimathi, M. Ranjitha, P. Malarkodi, S. Nallusamy, Efficient Classification of Heart Disease using KMeans Clustering Algorithm, International Journal of Engineering Trends and Technology., 68(12) (2020) 48-53. ISSN: 2231 – 5381 /doi:10.14445/22315381/IJETT-V68I12P209.
[22] Aman, Rajender Singh Chhillar, Disease Predictive Models for Healthcare by using Data Mining Techniques: State of the Art, International Journal of Engineering Trends and Technology, 68(10) (2020) 52-57. ISSN: 2231 – 5381 /doi:10.14445/22315381/IJETTV68I10P209.