Application of Medical Data Mining on the Prediction of APACHE II Score

The Acute Physiology and Chronic Health Evaluation (APACHE II) is a beneficial tool for the estimation of risk and the comparison of the patients who received care with similar risk properties. Machine learning based systems can assist clinicians in the early diagnosis of diseases. This research aimed at predicting the APACHE II score using Support Vector Machine (SVM) from Medical Data Mining (MDM). The records of 280 patients from intensive care unit included the dataset containing the target variable (the APACHE II score), and 23 demographical/clinical predictor variables. Genetic algorithm based feature selection and 10-fold cross validation method were employed. SVM with radial basis (RBF) was constructed. The performance of the proposed approach was assessed using root mean squared error (RMSE), mean absolute error (MAE), correlation (R) and coefficient of determination (R2). Mean age of the individuals was 51±23 years. 153 (54.6%) were females, and 127 (45.4%) were males. The proposed approach yielded the values of 1.037 for RMSE, 0.727 for MAE, 0.993 for R and 0.986 for R, respectively. The results demonstrated that the proposed approach had an excellent predictive performance of the APACHE II score. Additionally, ensemble approaches such as bagging, boosting, voting etc. can improve markedly the performance of the prediction and classification tasks.

___

1. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818-29.

2. Darrien J, Kasem H. Minimally invasive endoscopic therapy for the management of Boerhaave's syndrome. Ann R Coll Surg Engl. 2013;95(8):552-6.

3. Relich M, Muszynski W. The use of intelligent systems for planning and scheduling of product development projects. Procedia Computer Science. 2014;35:1586-95.

4. Zhu L, Wu B, Cao C. [Introduction to medical data mining]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2003;20(3):559-62.

5. Soni J, Ansari U, Sharma D, Soni S. Predictive data mining for medical diagnosis: An overview of heart disease prediction. IJCA. 2011;17(8):43-8.

6. IBM SPSS Modeler 15 Algorithms Guide. 2012:3-7. ftp://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/15.0/en/Algo rithmsGuide.pdf access date 12.02.2015

7. Raza K, Hasan AN. A comprehensive evaluation of machine learning techniques for cancer class prediction based on microarray data. arXiv preprint arXiv:13077050. http://arxiv.org/ftp/arxiv/papers/1307/1307.7050.pdf access date 12.02.2015

8. Akthar F, Hahne C. RapidMiner 5 Operator Reference. Rapid-I GmbH. 2012. https://rapidminer.com/wpcontent/uploads/2013/10/RapidMiner_OperatorReference_en.pdf access date 12.02.2015

9. Tang J, Alelyani S, Liu H. Feature selection for classification: a review. data classification: algorithms and applications. In: Aggarwal C, ed, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press. 2014.

10. Yu L, Liu H, eds, Feature selection for high-d imensional data: A fast correlationbased filter solution. Washington: ICML. 2003; 856-63.

11. Janc K, Tarasiuk J, Bonnet A, Lipinski P. Genetic algorithms as a useful tool for trabecular and cortical bone segmentation. Comput Methods Programs Biomed. 2013;111(1):72-83.

12. Vapnik VN, Vapnik V. Statistical learning theory: Wiley, New York, 1998.

13. Hsu C-W, Chang C-C, Lin C-J. A practical guide to support vector classification. 2003. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf access date 12.02.2015

14. Shih FY, Zhang K. Support vector machine networks for multi-class classification. International Journal of Pattern Recognition and Artificial Intelligence. 2005;19(06):775-86.

15. Song Q, Hu W, Xie W. Robust support vector machine with bullet hole image classification. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on. 2002;32(4):440-8.

16. Acır N, Güzeliş C. Automatic spike detection in EEG by a two-stage procedure based on support vector machines. Comput Biol Med. 2004;34(7):561-75.

17. Zararsiz G, Elmali F, Ozturk A. Bagging support vector machines for leukemia classification. Development. 2012. http://biorxiv.org/content/biorxiv/early/2014/07/28/007526.full.pdf access date 12.02.2015

18. Liu L, Shen B, Wang X. Research on kernel function of support vector machine. In: Advanced Technologies, Embedded and Multimedia for Human-centric Computing. Hannover: Springer. 2014;827-34

19. Cameron AC, Windmeijer FA. An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of Econometrics. 1997;77(2):329-42.
Medicine Science-Cover
  • ISSN: 2147-0634
  • Yayın Aralığı: 4
  • Başlangıç: 2012
  • Yayıncı: Effect Publishing Agency ( EPA )
Sayıdaki Diğer Makaleler

Would Anti-Hypertensive Treatment be a Risk Factor for National Health Budget of Turkey in 2023?

MEHMET BİLGEHAN PEKTAŞ, Mustafa ALDEMİR, AYHAN PEKTAŞ, Önder AKÇİ, İsmet DOĞAN

Fluoride Anticoagulant Vials are Ideal for HbA1c Estimation in India

Devajit SARMAH, Booloo SHARMA, Chandan Kr NATH

Cochlear Mechanisms in Noise Induced Hearing Loss

MUHAMMED SEDAT SAKAT, ALİ SAMİ BERÇİN, Korhan KILIÇ

Laparoscopic Evaluation of Nonspecific Abdominal Pain in Females

Syed Mushtaq SHAH, Azhar MUSHTAQ, Hanief Mohamed DAR, Wasim QADİR

May Ursodeoxycholic Acid Significantly Improve Liver Function Tests among Patients with Hepatitis C?

ALPASLAN TANOĞLU, Ümit SAVAŞCI, Ergenekon KARAGÖZ

The effects of mineral water drinking and extra-abdominal pressure in elimination of attenuation artifacts on myocardial perfusion SPECT

Fatih BATİ, Ersoy KEKİLLİ, Ismail KOKSAL, Vedat SUBASİ

Pulsating Enophthalmos in Neurofibromatosis Type 1

Yıldıray YILDIRIM, Eyüp DÜZGÜN, Serkan ARIBAL, Taner KAR, Mehmet SABAHYILDIZI

Distal Renal Tubular Acidosis Presenting as Recurrent Paralytic Crises A Case Report

Rupali MALİK, Nabadwip PATHAK

Association of Lack of Dreaming and Forgetfulness: Presentation of a Case with Depression

Behice Han ALMİŞ, MUSTAFA ÇELİK, Birgül Elbozan CUMURCU, Aysun KALENDEROĞLU

The Relationship Between Hepatic Activity Index and Serum Tumor Necrosis Factor Alpha Levels in Patients with Chronic Active Hepatitis-B and Chronic Active Hepatitis-C

Yılmaz BİLGİÇ, MUHSİN MURAT MUHİP HARPUTLUOĞLU, Bülent YAPRAK, Mehmet Burak DAL, MEHMET BUĞRA BOZAN, ABDURRAHMAN ŞAHİN, FATİH MEHMET YAZAR, Yasir Furkan ÇAĞIN, Nihat OKÇU