Application Tool based on C4.5 Decision Tree for Diagnosing Diabetes Infection Symptoms

Amel Hameed Khaleel, Ghaida A. Al-Suhai, Bushra M. Hussan


Abstract—Diabetes is a fatal disease which can lead to many other dangerous illnesses such as blindness, hypertension, kidney failure, heart attacks, and gangrene.   Nowadays, the intelligent diabetic systems exploit important tools to both medical industry and diabetes patients due to their crucial role in improving the quality of healthcare in many ways. Such systems can be considered as a very helpful tool to allow the physicians and the doctors intervene to find the proper treatment for the patient with all kinds of conditions. This paper therefore presents an application of intelligent system for diabetes detection symptoms to support and give advice to clinical management and patients. It basically relies on how to detect and find the Probability of Infection Diabetes. Hence, if a person suffers from the symptoms of a group, a patient will be referred to the possibility of diabetes. System diagnostics are examined based on the algorithm of Szajnar and Setla. It starts whether there is infect or not infect/doubt in the possibility of injury. When the person is doubted with the probability of injury, the probability of injury with symptoms can pass through a set of resulting rules from the C4.5 decision tree algorithm. The results reveal the finding ratios of Incidence percentage by the sum of the values of the outputs of the rules derived. It was ascertained the validity of the results by comparing them with the Indian diabetes database whereas if the injury rate of less than 50 is not infected and greater or equal to 50 is infected.  Consequently, the implementation of this expert application tool shows very good results.


Diabetic Diagnosis; Data Mining, Intelligence Tools; C4.5 Classifiers; Healthcare; Medical Expert System

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Ibrahim M.Ahmeda, Marco Alfonseb, Mostafa Arefc ,Abdel-Badeeh M.Salemd, “Reasoning Techniques for Diabetics Expert Systems”, Procedia Computer Science ( 813 – 820),2015.

Ibrahim M. Ahmed, Abeer M. Mahmoud, “Development of an Expert System for Diabetic Type-2 Diet”, International Journal of Computer Applications (0975 – 8887) Volume 107 – No.1, December 2014.

Dr. Abdullah Al-Malaise Al-Ghamdi, Majda A.Wazzan, Fatimah M. Mujallid, Najwa K.Bakhsh, “An Expert System of Determining Diabetes Treatment Based on Cloud Computing Platforms”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2 (5) , 2011.

Ibrahim M.Ahmed, Abeer M.Mahmoud, Mostafa Aref, Abdel-Badeeh M.Salem, “A study on Expert Systems for Diabetic Diagnosis and Treatment”, Recent Advances in Information Science, ISBN: 978-960-474-304-9,2012.

P. M. Beulah Devamalar, V. Thulasi Bai, andSrivatsa S. K., “An Architecture for a FullyAutomated Real-Time Web-Centric Expert System”, World Academy of Science, Engineering and Technology, 2007.

Cindy.M, Jay. Sand Frank. S,”Toward Case Based Reasoning For Diabetes Management”, Computational Intelligence Journal, Volume 25, Number 3,9p 165-179, 2009.

Matthew Wiley and Razvan Bunescu. “Emerging Applications for Intelligent Diabetes Management Cindy Marling”, Association for the Advancement of Artificial Intelligence, 2011.

Wioletta SZAJNAR and Galina SETLAK., “A concept of building an intelligence system to support diabetes diagnostics”, Studia Informatica, 2011.

M.Kalpana and A.V Senthil Kumar,”Fuzzy Expert System for Diabetes using Fuzzy Verdict Mechanism”, Int. J. Advanced Networking and Applications Volume: 03, Issue: 02, Pages: 1128-1134 , 2011.

Sanjeev Kumar and Babasaheb Bhimrao, “Development of knowledge Base Expert System for Natural treatment of Diabetes disease”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 3, 2012.

N.Nnamoko, F.Arshad, D.England and J. Vora, “Fuzzy Expert System for Type 2 Diabetes Mellitus (T2DM) Management Using Dual Inference Mechanism”, AAAI Spring Symposium, p 67-70, 2013.

Margret Anouncia S., Clara Madonna L., Jeevitha P.and Nandhini R.,”Design of a Diabetic Diagnosis System Using Rough Sets, Cybernetics And Information Technologies” ,Volume 13, No 3, pp 124-139,Sofia ,2013.

Abdelhak .M, Baghdad .A, and Sofia. B, “A Hybrid Decision Support System :Application On Healthcare “, Corr, 2013.

C. Marling, S.Montani , I. Bichindaritz and Peter Funk, “Synergistic case-based reasoning in medical domains, Expert Systems with Applications”, Volume 41, Issue 2, 1, Pages 249–259, February 2014.

VelidePhani Kumar 1 and Lakshmi Velide2, “A Data Mining Approach For Prediction And Treatment Ofdiabetes Disease” , VelidePhani Kumar-et al., IJSIT, 3(1),073-0792014.

M. Berry and M. Browne, “Lecture Notes in Data Mining”, published by World Scientific, United States of America, 2006.

J. Han and M. Kamber, “Data Mining: Concepts and Techniques”, Second Edition, published by Elsevier, United States of America, 2006.

M. L. Wong and K. S. Leung , “Data Mining Using Grammar Based Genetic Programming and Applications”, published by Kluwer Academic, United States of America, 2002.

T. S. Korting , “C4.5 algorithm and Multivariate Decision Trees”, In proceeding on National Institute for Space Research, PP.(1-5), Brazil,, 2006.

D. T. Larose, “Discovering Knowledge in Data”, published by JohnWiley & Sons, United States of America, 2005.

D. Benhaddouche and A. Benyettou, “Data mining by the decision tree and support vector machine (SVM)”, In proceeding of 5th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications(SETIT), TUNISIA, PP. (1-5), (22-26) March 2009.

S. Theodoridis and K. Koutroumbas, “Pattern Recognition”, Second Edition, published by Elsevier, United States of America, 2003.

Z. Markov and D. T. Larose, “Data Mining The Web”, published by John Wiley & Sons, United States of America, 2007.

R. Devi and V. Khemchandani, “Application of Data Mining Techniques For Diabetic Dataset”, Proceedings of the 4th National Conference, Computing For Nation Development, Bharati Vidyapeeth’s Institute of Computer Applications and Management, New Delhi, February 25 – 26, 2010.

J.R. Quinlan , “Induction of Decision Trees”, In proceeding of Centre for Advanced Computing Sciences, New South Wales Institute of Technology, Australia, PP.(81-106), 2007.

Sumathy, Mythili, P. Kumar, T. M. Jishnujit, and K.R. Kumar, “Diagnosis of Diabetes Mellitus based on Risk Factors”, In proceeding of International Journal of Computer Applications, Vol. (10), No. (4), PP. (1-4), 2010.

A. R. Webb, “Statistical Pattern Recognition”, Second Edition, published by John Wiley & Sons, England, 2002.

W. Szajnar and G. Setlak, “A Concept of Design Process of Intelligent System Supporting Diabetes Diagnostics”, In proceeding of Methods and Instruments of Artificial Intelligence, PP. (168-178), 2010.