Principal Component Analysis Using For Estimating Hypertension

Year 2020, Volume: 12 Issue: 3, 42 - 51, 31.12.2020

Halil Murat Ünver , Yunus Kökver , Aydın Çifci

https://doi.org/10.29137/umagd.685928

Abstract

Aim: 150 patients which aged 30 years and over were exposed to possible hypertension; age, gender, lipid profile, body mass
index, triglyceride , cigarette use and uric acid data are collected and hypertension database are created. 65 people is healthy, and
the remaining one is suffering from hypertension. It is aimed to estimate the hypertension disease from this database using the
Principal Component Analysis.
Material and Method: Decision Table, Naive Bayes, C4.5 and Multilayer Perceptron Network(MLP) classification algorithms
are applied to this database, then the size of the hypertension database is reduced by applying Principal Component Analysis and
the same methods are applied again and the results are compared.
Results: The most successful result of the algorithms that were processed under the same conditions gave Naive Bayes classifier
with 88% accuracy. Naive Bayes classifier was followed by the Decision Table algorithm with success rate of 85.33%, and ÇKA
algorithms with success rate of 82.67%. If the TBA analysis is applied to the hypertension database and the same algorithms are
re-processed under the same conditions and the TBA is compared to the untreated results, the C4.5 algorithm is normally the
most successful algorithm with 4% more successful results. The Decision Table algorithm, which yielded C4.5 algorithm with
2.67% more success rate respectively, and ÇKA which has a more successful result than 1.33%.
Conclusion: Algorithms except the Naive Bayes algorithm, improved their classification accuracy rate

Keywords

Hypertension, principal component analysis, data mining, decision tree

Hipertansiyon Tahmini İçin Temel Bileşen Analizinin Kullanımı

Abstract

Amaç: Otuz yaş ve üzerindeki 150 hastadan, hipertansiyona etki etmesi muhtemel bilgilerden; cinsiyet, yaş, lipid profili,
trigliserid, vücut kütle indeksi, ürik asit ve sigara kullanımı verileri toplanmış ve bir hipertansiyon veritabanı oluşturulmuştur. Bu
kişilerden 65’i sağlıklı, geriye kalan 85 kişi ise hipertansiyon hastasıdır. Bu veritabanından hipertansiyon hastalığının Temel
Bileşen Analizi kullanılarak tahmin edilmesi amaçlanmıştır.
Gereç ve Yöntem: Naive Bayes, Çok Katmanlı Algılayıcı Ağ (ÇKA), Karar Tablosu ve C4.5 sınıflandırma algoritmaları
uygulanmış, ardından Temel Bileşenler Analizi uygulanarak hipertansiyon veritabanının boyutu indirgenmiş ve aynı
sınıflandırma algoritmaları tekrar uygulanmış ve sonuçlar karşılaştırılmıştır.
Bulgular: Aynı şartlarda işleme sokulan algoritmalardan en başarılı sonucu %88 doğruluk oranıyla Naive Bayes sınıflandırıcısı
vermiştir. Naive Bayes sınıflandırıcısını sırasıyla %85,33 başarı oranıyla Karar Tablosu algoritması, %82,67 başarı oranıyla
ÇKA algoritmaları takip etmiştir. Hipertansiyon veritabanına TBA analizi uygulanıp, aynı şartlarda aynı algoritmalar tekrar
işleme sokulup, TBA uygulanmayan sonuçlarla kıyaslandığında ise, C4.5 algoritması normalden %4 daha başarılı sonuç vererek
en başarılı algoritma olmuştur. C4.5 algoritmasını sırasıyla %2,67 daha başarılı sonuç veren Karar Tablosu algoritması ve %1,33
daha başarılı sonuç veren ÇKA izlemiştir.
Sonuç: Naive Bayes sınıflandırıcı haricindeki tüm algoritmalarda Temel Bileşenler Analizi’nin sınıflandırma başarısını artırdığı
görülmüştür. 

Keywords

Hipertansiyon, temel bileşen analizi, veri madenciliği, Karar ağacı

References

• 1. World Health Organization. A global brief on Hypertension. Silent killer, global public health crisis. World Health Day 2013. Available: http://apps.who.int/iris/bitstream/10665/79059/1/ WHO_ DCO_WHD_2013.2_eng.pdf?ua=1 2. Carretero OA, Oparil S. Essential hypertension. Part I: definition and etiology Circulation 2000; 101; 3: 329-35. 3. Chae YM, Ho SH, Cho KW, Lee DH, Ji SH. Data mining approach to policy analysis in a health insurance domain. Int J Med Inform 2001; 62; 2-3: 103-11. 4. Almazyad AS, Ahamad MG, Siddiqui MK, Almazyad AS. Effective hypertensive treatment using data mining in saudi arabia. journal of clinical monitoring and computing 2010; 24; 6: 391-401. 5. Ture M, Kurt I, Kurum AT, Ozdamar K. Comparing classification techniques for predicting essential hypertension. Expert Systems with Applications 2005; 29: 583-8. 6. Aljumah A, Siddiqui MK. Hypertension Interventions using Classification Based Data Mining. Research Journal of Applied Sciences, Engineering and Technology 2014; 7; 17: 3593-602. 7. Türk F, Barişçi N, Çiftçi A, Ekmekçi Y. Comparison of Multi Layer Perceptron and Jordan Elman Neural Networks for Diagnosis of Hypertension. Intelligent Automation & Soft Computing 2015; 21; 1: 123-34. 8. Kökver Y, Barisci N, Çiftçi A, Ekmekçi Y. Determining Affecting Factors of Hypertension with Data Mining Techniques. Engineering Sci 2014; 9; 2:15-25. 9. Chalmers J. et al. 1999 World Health Organization International Society of Hypertension guidelines for the management of hypertension. J Hypertens 1999; 17; 2: 151-83. 10. Chobanian AV. et al. Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension 2003; 42; 6: 1206-52. 11. Demuth HB, Beale MH, De Jess O, Hagan MT. Neural network design. Martin Hagan, 2014. 12. Sancho-Gómez JL, García-Laencina PJ, Figueiras-Vidal AR. Combining missing data imputation and pattern classification in a multi-layer perceptron. Intelligent Automation & Soft Computing 2009; 15; 4: 539-53. 13. Haykin S. Neural networks: a comprehensive foundation 1994; Prentice Hall PTR. 14. Bishop C. Neural Networks for Pattern Recognition 1995; Oxford Univ. Press, N Y. 15. Quinlan J. C4. 5: Programs for machine learning Morgan Kaufmann Publishers San Francisco 1993; CA Google Scholar. 16. Korting TS. C4. 5 algorithm and multivariate decision trees Image Processing Division, National Institute for Space Research–INPE Sao Jose dos Campos–SP, Brazil 2006. 17. Wang J. Encyclopedia of data warehousing and mining. IGI Global, 2005. 18. Gandhi M. Srivatsa S. Classification algorithms in comparing classifier categories to predict the accuracy of the network intrusion detection–a machine learning approach. Advances in Computational Sciences and Technology 2010; 3; 3. 19. Berkhin P. Survey of Clustering Data Mining Techniques. Accrue Software, Inc., San Jose ed: USA, Tech. Rep.2002; 68. 20. Tang L, Zhou XJ. Diffusion MRI of cancer: From low to high b-values. J Magn Reson Imaging. 2018 Oct 12. doi: 10.1002/jmri.26293. 21. Abd El Baky Mahmoud M, Shaaban MAA, Ali Ramzy A. Clinical role of serum Copeptin in acute coronary syndrome. Egypt Heart J. 2018 Sep; 70: 155-9. doi: 10.1016/j.ehj.2018.04.008. 22. Li J, Wang L, Wang Q, Xin Z, Liu Y, Zhao Q. Diagnostic value of carotid artery ultrasound and hypersensitive C-reactive protein in Type 2 diabetes mellitus patients with acute myocardial infarction in Chinese population. Medicine (Baltimore). 2018 Oct; 97: e12334.