Snigdho Dip Howlader; Tushar Biswas; Aishwarjyo Roy; Golam Mortuja; Dip Nandi

A Comparative Analysis of Algorithms for Heart Disease Prediction Using Data Mining

Full Text (PDF, 331KB), PP.45-54

Views: 0 Downloads: 0

Author(s)

Snigdho Dip Howlader ^1,* Tushar Biswas ¹ Aishwarjyo Roy ¹ Golam Mortuja ¹ Dip Nandi ²

1. Department of Computer Science, American International University - Bangladesh, Dhaka, Bangladesh

2. Faculty of Science and Technology, American International University-Bangladesh, Dhaka, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2023.05.05

Received: 23 Mar. 2023 / Revised: 15 Jun. 2023 / Accepted: 20 Jul. 2023 / Published: 8 Oct. 2023

Index Terms

Heart Disease Predictions, Machine Learning, Data Mining

Abstract

Heart disease is very common in today’s day and age, with death rates climbing up the numbers every year. Prediction of heart disease cases is a topic that has been around in the world of data and medical science for many years. The study conducted in this paper makes comparison of the different algorithms that have been used in pattern analysis and prediction of heart diseases. Among the algorithms that have been used in the past included a combination of machine learning and data mining concepts that essentially are derived from statistical analysis and relevant approaches. There are a lot of factors that can be considered when attempting to analytically predict instances of heart diseases, such as age, gender, resting blood pressure etc. Eight such factors have been taken into consideration for carrying out this qualitative comparison. As this study uses a particular data set for extracting results from, the output may vary when implemented over different data sets. The research includes comparisons of Naive Bayes, Decision Tree, Random Forest and Logistic Regression. After multiple implementations, the accuracy in training and testing are obtained and listed down. The observations from implementation of these algorithms over the same dataset indicates that Random Forest and Decision Tree have the highest accuracy in prediction of heart disease based on the dataset that we have provided. Similarly, Naive Bayes has the least accurate results for this scenario under the given contexts.

Cite This Paper

Snigdho Dip Howlader, Tushar Biswas, Aishwarjyo Roy, Golam Mortuja, Dip Nandi, "A Comparative Analysis of Algorithms for Heart Disease Prediction Using Data Mining", International Journal of Information Technology and Computer Science(IJITCS), Vol.15, No.5, pp.45-54, 2023. DOI:10.5815/ijitcs.2023.05.05

Reference

[1]Chaitrali S. Dangare, Sulabha S. Apte, PhD. “Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques”, International Journal of Computer Applications (0975 – 888) Volume 47– No.10, June 2012
[2]V. Krishnaiah, G. Narsimha, and N. Subhash Chandra “Heart Disease Prediction System Using Data Mining Technique by Fuzzy K-NN Approach”, Springer International Publishing Switzerland 2015
[3]Devansh Shah, Samir Patel, Santosh Kumar Bharti, “Heart Disease Prediction using Machine Learning Techniques”, Springer Nature Singapore Pte Ltd 2020
[4]R Fadnavis, K Dhore, D Gupta, J Waghmare and D Kosankar, “Heart disease prediction using data mining”, International Conference on Research Frontiers in Sciences (ICRFS), 2021
[5]Boshra Bahrami, Mirsaeid Hosseini Shirvani, “Prediction and Diagnosis of Heart Disease by Data Mining Techniques”, Journal of Multidisciplinary Engineering Science and Technology (JMEST) ISSN: 3159-0040 Vol. 2 Issue 2, February - 2015
[6]S. Ramasamy, K. Nirmala, “Disease prediction in data mining using association rule mining and keyword based clustering algorithms”, International Journal of Computers and Application, 2017
[7]Pavithra M., Sindhana A.M, Subajanaki T. et al.“Effective Heart Disease Prediction Systems Using Data Mining Techniques”, Annals of R.S.C.B., ISSN:1583-6258, Vol. 25, Issue 3, 2021
[8]M. A. Nishara Banu, B. Gomathy, “Disease Predicting System Using Data Mining Techniques”, International Journal of Technical Research and Applications, 2013
[9]Anurag Kumar Verma, Saurabh Pal, Surjeet Kumar, “Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method—a Comparative Study”, Applied Biochemistry and Biotechnology, 2019
[10]Sellappan Palaniappan, Rafiah Awang, “Intelligent Heart Disease Prediction System Using Data Mining Techniques”, IEEE, 2008
[11]Sarath Babu, Vivek EM, Famina KP, Fida K, Aswathi P, Shanid M, Hena M, “Heart Disease Diagnosis Using Data Mining Technique”, International Conference on Electronics, Communication and Aerospace Technology, 2017
[12]Sneha Grampurohit, Chetan Sagarnal, “Disease Prediction using Machine Learning Algorithms”, International Conference for Emerging Technology, 2020
[13]M. A. Nishara Banu, B. Gomathy, “Disease Forecasting System Using Data Mining Methods”, International Conference on Intelligent Computing Applications, 2014
[14]Hasib Qaiser, “Data Mining in Healthcare for Heart Diseases”, International Journal of Innovation and Applied Studies, 2015
[15]Senthilkumar Mohan, Chandrasegar Thirumalai, Gautam Srivastava, “Effective Heart Disease Prediction using Hybrid Machine Learning Techniques”, IEEE Access, 2017
[16]Lahiru Iddamalgoda, Partha S. Das, Achala Aponso, Vijayaraghava S. Sundararajan, Prashanth Suravajhala and Jayaraman K. Valadi, “Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications”, Frontiers in Genetics, 2016
[17]Rohit Bharti, Aditya Khamparia, Mohammad Shabaz, Gaurav Dhiman, Sagar Pande, Parneet Singh, “Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning”, Hindawi, 2021
[18]M. A. Nishara Banu, B. Gomathy, “Disease Forecasting System Using Data Mining Methods”, International Conference on Intelligent Computing Applications, 2014
[19]V. Krishnaiah, G. Narsimha, Ph.D., N. Subhash Chandra, Ph.D., “Heart Disease Prediction System using Data Mining Techniques and Intelligent Fuzzy Approach: A Review”, International Journal of Computer Applications, 2016
[20]Sravani Nalluri, Vijaya Saraswathi Redrowthu, Somula Ramasubbareddy, Kharisma Govinda, E. Swetha, “Chronic Heart Disease Prediction Using Data Mining Techniques”, Data Engineering and Communication Technology, 2020
[21]Umair Shafique, “Data Mining in Healthcare for Heart Diseases”, International Journal of Innovation and Applied Studies, 2015
[22]Divya Tomar, Sonali Agarwal, “A survey on Data Mining approaches for Healthcare”, International Journal of Bio-Science and Bio-Technology, 2013
[23]Ibomoiye Domor Mienyea, Yanxia Suna, Zenghui Wang, “An Improved Ensemble Learning Approach for the Prediction of Heart Disease Risk”, Elsevier, 2020
[24]Md Mamun Ali, Bikash Kumar Paul, Kawsar Ahmed, Francis M. Bui, Julian M. W. Quinn, Mohammad Ali Moni, “Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison”, Elsevier, 2021
[25]Neelam Singh, Santosh Kumar Singh Bhadauria, “Early Detection of Cancer Using Data Mining”, International Journal of Applied Mathematical Sciences, 2016
[26]Aqueel Ahmed, Shaikh Abdul Hannan, “Data Mining Techniques to Find Out Heart Diseases: An Overview”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2012
[27]Mirpouya Mirmozaffari, Alireza Alinezhad, Azadeh Gilanpour, “Data Mining Apriori Algorithm for Heart Disease Prediction'', Int'l Journal of Computing, Communications & Instrumentation Engg. (IJCCIE), 2017
[28]Michael J. Pencina, Ann Marie Navar, Daniel Wojdyla, MS Robert J. Sanchez, Irfan Khan, Joseph Elassal, Ralph B. D’Agostino Sr, Eric D. Peterson, Allan D. Sniderman, “Quantifying Importance of Major Risk Factors for Coronary Heart Disease”, Circulation - The American Heart Association Inc., 2019
[29]Mary K. Obenshain, MAT, "Application of Data Mining Techniques to Healthcare Data”, The Society of Healthcare Epidemiology of America, 2014
[30]Qi Rong Huang, Ph.D. Zhenxing Qin, Ph.D., Shichao Zhang, Ph.D., Chin Moi Chow, Ph.D., “Clinical Patterns of Obstructive Sleep Apnea and Its Comorbid Conditions: A Data Mining Approach”, Journal of Clinical Sleep Medicine, 2008
[31]Moloud Abdar, Sharareh R. Niakan Kalhori, Tole Sutikno, Imam Much Ibnu Subroto, Goli Arji, “Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases”, International Journal of Electrical and Computer Engineering (IJECE), 2015
[32]C. Beulah Christalin Latah, S. Carolin Jeeva, “Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques”, Elsevier, 2019
[33]Haleh Ayatollahi, Leila Gholamhosseini, Masoud Salehi, “Predicting coronary artery disease: a comparison between two data mining algorithms”, BMC Public Health, 2019
[34]Apurb Rajdhan, Milan Sai, Avi Agarwal, Dundigalla Ravi, “Heart Disease Prediction using Machine Learning”, International Journal of Engineering Research & Technology (IJERT), 2020
[35]Vikas Chaurasia, Saurabh, “Data Mining Approach to Detect Heart Diseases”, International Journal of Advanced Computer Science and Information Technology (IJACSIT), 2013
[36]Uma N. Dulhare, “Prediction System for Heart Disease using Naive Bayes and Particle Swarm”, Biomedical Research, 2018
[37]Cincy Raju, Philipsy E, Siji Chacko, L Padma Suresh, Deepa Rajan S, “A Survey on Predicting Heart Disease using Data Mining Techniques”, IEEE Conference on Emerging Devices and Smart Systems, 2018
[38]Yeshvendra K. Singh, Nikhil Sinha, Sanjay K. Singh, “Heart Disease Prediction System Using Random Forest”, Springer Nature Singapore Pte Ltd., 2017
[39]Vikas Chaurasia, Saurabh Pal, “Data Mining Approach to Detect Heart Diseases”, International Journal of Advanced Computer Science and Information Technology (IJACSIT), 2013
[40]Mohammed Khalilia, Sounak Chakraborty, Mihail Popescu, “Predicting disease risks from highly imbalanced data using random forest”, BMC Medical Informatics & Decision Making, 2011

International Journal of Information Technology and Computer Science (IJITCS)