Predicting the Effectiveness of Preeclampsia Medications Based on Dose and Method of drug Consumption Using Data Mining

Jurian, Najmeh; Ashoori, Maryam

doi:10.22038/ijogi.2014.3588

Predicting the Effectiveness of Preeclampsia Medications Based on Dose and Method of drug Consumption Using Data Mining

Document Type : Original Article

Authors

Najmeh Jurian ¹
maryam Ashoori ²

¹ M.Sc. of E-MBA, Hakim Hospital, Neyshabour Medical Sciences and Health Services , Iran.

² M.Sc. of Information Technology engineering, School of Industrial Engineering, K. N. Toosi University, Tehran, Iran.

10.22038/ijogi.2014.3588

Abstract

Introduction: Preeclampsia is a serious disorder during pregnancy that even can lead to termination of pregnancy and fetus abortion. This study was performed with the aim to generate an efficient predictive model to accurately predict the effectiveness of preeclampsia medications.
Methods: This is a descriptive, cross-sectional study performed on 774 patients in Neyshabour Hakim Hospital from 2011-2013. Three algorithms, C5.0, C & R Tree and CHAID were applied on data of preeclampsia, and then the accuracy of generated models was obtained. In this study, Clementine software (version 12.0) was used for data analysis and implementation of data mining algorithms.
Results: The obtained values for accuracy of generated models by implementation of C5.0, C&R Tree and CHAID algorithms on train and test dataset were 99.63 and 99.14, respectively. The high accuracy of models showed good performance of these algorithms. The sensitivity of 100% and precision of 99.5% confirmed that C&R Tree is superior to other algorithms. Obtained results of clustering on better tree showed that when the effectiveness of medicine has been increased, the patient's mean age was increased, and it was due to direct relation between preeclampsia and increasing age.
Conclusion: The obtained results show that the probability of preeclampsia is increased with increasing age and the medications with high effectiveness should be used for treatment. Therefore, more attention should be taken for selecting the used medicine with different effectiveness. Prediction of the effectiveness of medicine is performed for achieving these targets: help to the doctor for increasing the accuracy of diagnosis and prevention of incorrect diagnosis in dose of medicine consumption for patient, diagnosing the preeclampsia severity and inhibition of dangerous effects of extra consumption of medication by patient, prediction of the adequate medicine storage and prevention of medicine lack side-effects.

Keywords

References

1. Li, B.N., Dong, M.C. Banking on Blood. IET Computing & Control Engineering, August/September 2006.
2. Ashoori,M., Naji Moghaddam,V., Alizadeh,S., Safi,M.Classification and Clustering Algorithm Application for Prediction of Tablet Numbers: Case Study Diabetes Disease. Health Information Management 1392; 10(5).
3. Rashidmehrabadi,A., Pedram,M. Classification and identification of blood donors tend to donate blood in the future. The Fourth Iran Data Mining Conference, Sharif University of Technology,2010,Tehran.
4. Ashoori, M., Taheri, Z. Using Clustering Methods for Identifying Blood Donors Behavior. 5th Iranian Conference on Electrical and Electronic Engineering (ICEEE), 20-22 August 2013, Gonabad, Iran, PP: 4055-4057.
5. Andrea L. Tranquilli, Beatrice Landi, Stefano R. Giannubilo, Baha M. Sibai. Preeclampsia: No longer solely a pregnancy disease, Pregnancy Hypertension: An International Journal of Women’s Cardiovascular Health, (2012), PP: 350–357.
6. Cathrine Staff,N., Circulating predictive biomarkers in preeclampsia, Pregnancy Hypertension: An International Journal of Women’s Cardiovascular Health, (2011), PP: 28–42.
7. Nikpour, S. Atarodi Kashani, Z. Mokhtarshahi, Sh. Parsay, S. Nooritajer, M. Haghani, H. Study of the Correlation of the Consumption of Vitamin C-Rich Foods with Preeclampsia and Eclampsia in Women Referred to Shahid Akbar Abadi Hospital in Tehran, Journal of Medical Sciences, 2004,Volume XIV / Issue 54.
8. Tabandeh,A., Argangi , H., Arabi, M . Serum hCG β in women with mild preeclampsia in pregnant women, Journal of Laboratory Medicine, 2013; Volume VII (1): 58-55.
9. Pennings, J.L.A., Kuc, S., Rodenburg, W., Koster, M.P.H., Schielen, P.C.J.I., Vries, A.D. Integrative data mining to identify novel candidate serum biomarkers for pre-eclampsia screening. Prenatal Diagnosis Journal, Vol. 31, No. 12, 2011, PP: 1153-1159.
10. Tabasi Z, Oghbaee N, Samimi M, Sadat Z. Comparison between Effects of Intravenous Labetalol and Hydralazine on Control of Hypertension and Maternal and Neonatal utcomes in Severe Preeclamptic Patients: A Randomized Clinical Trial. Qom Univ Med Sci J 2013;6(4):44-49.
11. Zabihi Mahmoodabadi A, Nobakht AR, Behrashi M, Musavi GH. Furosemide versus hydralazine for managing post partumhy pertension in severe preeclampsia: a comparative study. J Shahid Sadoughi Univ Med Sci 2012; 20(4): 482-88.
12. Modeling techniques in Clementine. Chapter11; Available from: URL:https://fhss.byu.edu/SPSS%20Modeler/Chapter%2011.pdf.
13. Rule Induction. Chapter12; Available from: URL: https://fhss.byu.edu/SPSS%20Modeler/Chapter%2012.pdf.
14. Khalilinezad,M., Minaee bidgoli,B. Data mining in medicine. The Third Iran Data Mining Conference,2009.
15. Bhardwaj, A., Sharma, A., and Shrivastava, V. Data Mining Techniques and Their Implementation in Blood Bank Sector –A Review. International Journal of Engineering Research and Applications (IJERA), Vol. 2, No. 4, July-August 2012, PP: 1303-1309.
16. Tan P.N, Steinbach M, Kumar V. Introduction to Data Mining. USA: Addison-Wesley Longman; 2005.
17. Ghazanfari,M., Alizade,S., Teymoorpour,B. Data Mining and Knowledge Discovery,2008,Iran University of Science and Technology.
18. Fiaschi, L., Garibaldi, J.M., Krasnogor, N. A Framework for the Application of Decision Trees to the Analysis of SNPs Data. IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB'09), 2009, PP: 106-113.
19. Liao S.-H, Chu P.-H, Hsiao P.-Y. Data mining techniques and applications – A decade review from 2000 to 2011. Expert Systems with Applications Journal, Vol. 39, 2012, PP: 11303-11311.
20. Toloie eshlaghy, A., Poor ebrahimi, A., Ebrahimi, M., Ghasem ahmad, L. Using Data Mining Techniques for Prediction Breast Cancer Recurrence. Iranian Journal of Diseases Breast, Vol. 5, No. 4, 2013, PP: 23-34.