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A New Approach: Role of Data Mining in Prediction of Survival of Burn Patients

Journal of Medical Systems volume 35, pages 1531–1542 (2011)Cite this article

Abstract

The prediction of burn patient survivability is a difficult problem to investigate till present times. In present study a prediction Model for patients with burns was built, and its capability to accurately predict the survivability was assessed. We have compared different data mining techniques to asses the performance of various algorithms based on the different measures used in the analysis of information pertaining to medical domain. Obtained results were evaluated for correctness with the help of registered medical practitioners. The dataset was collected from SRT (Swami Ramanand Tirth) Hospital in India, which is one of the Asia’s largest rural hospitals. Dataset contains records of 180 patients mainly suffering from burn injuries collected during period from the year 2002 to 2006. Features contain patients’ age, sex and percentage of burn received for eight different parts of the body. Prediction models have been developed through rigorous comparative study of important and relevant data mining classification techniques namely, navie bayes, decision tree, support vector machine and back propagation. Performance comparison was also carried out for measuring unbiased estimate of the prediction models using 10-fold cross-validation method. Using the analysis of obtained results, we show that Navie bayes is the best predictor with an accuracy of 97.78% on the holdout samples, further, both the decision tree and support vector machine (SVM) techniques demonstrated an accuracy of 96.12%, and back propagation technique resulted in achieving accuracy of 95%.

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Acknowledgement

The authors are highly thankful to reviewers for their fruitful comments and suggestions which helped us to improve our earlier versions of the paper and also thankfull to B.I.Khdakbhavi Director of M.B.E.Society’s College of engineering ambajogai for their sponsorship and AICTE.

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Affiliations

  1. Indian Institute of Technology, Roorkee, India

    Bankat Madhavrao Patil, Ramesh C. Joshi & Durga Toshniwal

  2. S.R.T. Rural Medical College Hospital Ambajogai, Ambajogai, India

    Siddeshwar Biradar

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  1. Bankat Madhavrao Patil
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  2. Ramesh C. Joshi
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  3. Durga Toshniwal
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  4. Siddeshwar Biradar
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Correspondence to Bankat Madhavrao Patil.

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Patil, B.M., Joshi, R.C., Toshniwal, D. et al. A New Approach: Role of Data Mining in Prediction of Survival of Burn Patients. J Med Syst 35, 1531–1542 (2011). https://doi.org/10.1007/s10916-010-9430-2

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  • DOI: https://doi.org/10.1007/s10916-010-9430-2

Keywords

  • Burn patients
  • Data mining
  • k-fold cross-validation
  • Prediction
  • Support Vector Machine (SVM)