A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm

Panigrahi Srikanth*, Ch.Anusha**, 0***
*-** PG Graduate, Department of Computer Science and Engineering, Vignan's Institute of Information Technology, Duvvada, Andhra Pradesh, India.
*** Professor, Department of Information Technology, Shri Vishnu Engineering College for Women, Bhimavaram, Andhra Pradesh, India.
Periodicity:March - May'2015
DOI : https://doi.org/10.26634/jcom.3.1.3438


Now-a-days humankind suffer from many health complications. People are affected by progressive diseases (like as Heart, Diabetes, AIDS, Hepatitis and Fibroid) and their complications. Data mining (also known as knowledge discovery) is the process of summarizing the data into useful information by analyzing data from different perspectives. Data Mining is a technology for processing large volume of data that combines traditional data analysis methods with highly developed algorithms. Data mining techniques can be used to support a wide range of security and business applications such as work flow management, customer profiling and fraud detection. It can be also used to predict the outcome of future observations. Data mining techniques can be developed by the Decision Tree Algorithm. According to a recent survey of the World Health Organization (WHO), all diseases and its complications are problematic health hazards of this century. A better and early diagnosis of disease may improve the lives of all people affected and people may lead healthy lives. In this paper, the authors present the Decision Tree Algorithm for better diagnosis of diseases using Association Rule mining. Using this computational intelligence technique the authors tested the performance of the method using disease data sets. The authors presented a better algorithm which is used to calculate sensitivity, specificity, comprehensibility and rule length. This gain and gain ratio achieved has promising accuracy.


Computational Intelligence Techniques, Decision Tree Algorithm, Data Sets, Heart Disease, Diabetes Disease, AIDS Disease, Hepatitis Disease, Fibroid Disease.

How to Cite this Article?

Srikanth, P., Anusha, Ch., and Devarapalli, D. (2015). A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm, i-manager’s Journal on Computer Science, 3(1), 21-26. https://doi.org/10.26634/jcom.3.1.3438


[1]. Han. J., Kamber. M., (2006). “Data Mining Concepts and Techniques,” Morgan Kaufmann Publishers, 2 Edition.
[2]. Jyoti Soni, Ujma Ansari, Dipesh Sharma, Sunita Soni, (2011). “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction,” International Journal on Computer Science and Engineering, Vol.17(8), pp.43-48.
[3]. John Shafer, Rakesh Agarwal, and Manish Mehta, (1996). “SPRINT: A Scalable Parallel Classifier for Data Mining,” In Proceedings of the VLDB Conference, pp.545- 555.
[4]. Jiawei Han, Micheline Kamber, (2001). “Data Mining: Concepts and Techniques,” 1 Edition.
[5]. http://archive.ics.uci.edu/ml/datasets/Heart+Disease
[6]. www.uci machine learning .com
[7]. Dharmaiah Devarapalli, Panigrahi Srikanth, (2014). “Identification of AIDS Disease Severity Using Genetic Algorithm,” Computational Intelligence Techniques for Comparative Genomics, pp.99-111.
[8]. Mai Shouman, Tim Turner, Rob Stocker, (2011). “Using Decision Tree for Diagnosing Heart Disease Patients,” Proceedings of the 9 Australasian Data Mining Conference (AusDM'11), Vol.121, pp.23-29.
[9]. D. Senthil Kumar, G. Sathyadevi and S. Sivanesh, (2011). “Decision Support System for Medical Diagnosis Using Data Mining,” International Journal of Computer Science Issues (IJCSI), Vol.8(3), pp.147-153.
[10]. ShravanKumar Uppin, Anusuya M A, (2014). “Expert System Design to Predict Heart and Diabetes Diseases,” International Journal of Scientific Engineering and Technology, Vol.3(8), pp.1054-1059.
[11]. Kamadi. VSRP Varma, Allam Apparao, P V Nageswar Rao, (2014). “A Computational Intelligence Technique for Effective and early Diabetes detection using Rough Set Theory,” International Journal of Computer Applications, Vol.95(11), pp.17-21.
[12]. Dharmaiah Devarapalli, Allam Apparao, Amit Kumar, G R Sridhar Helix, (2013). “A Novel Analysis of Diabetes Mellitus by Using Expert System based on Brain Derived Neurotrophic Factor (BDNF) Levels,” GRS Dharmaiah Devrapalli, Vol.1, pp.251-256 .
[13]. Girija D.K, Dr. M.S. Shashidhara, (2012). “Comparison of Fibroid Syndrome using Data Mining Techniques,” International Journal of Emerging Technology and Advanced Engineering, Vol.2(11), pp.347-352.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.