Framingham Risk Score by Data Mining Method

Yıl 2020, Cilt: 8 Sayı: 3, 70 - 75, 01.10.2020

Şükrü Kitiş

https://doi.org/10.18100/ijamec.795224

Öz

There are cleaning, integration, reduction, conversion, algorithm implementation and evaluation stages in data mining meaning finding necessary data from a wide variety of variables and data. It is important to create a data warehouse to realize these steps. Data randomly selected from data warehouse is evaluated with certain algorithms. While deaths resulting from heart diseases in our country are 37% according to 2016 data, 420-440 thousand people are diagnosed as heart patients each year and the number of deaths per year can reach 340 thousand people. These values correspond to approximately three times of Europe. In this study, risk of heart attack is calculated by data mining method by taking advantage of Framingham risk score. In order to determine this risk factor; 10-year risk is calculated by looking at sex, age, total cholesterol, HDL cholesterol, blood pressure, diabetes and smoking. While the effects of the ages for men starts -9 points, ends with +13 points and for women starts -7 points, ends with +16 points. While the effects of the total cholesterol for men starts 0 points, ends with +11 points and for women starts 0 points, ends with +13 points. Total scores are between 0-17 and over in men, and scores between 0-25 and over in women. There are risk values ranging from 1% to 30%.

Anahtar Kelimeler

Data mining, Heart attack risk, Framingham risc score

Kaynakça

• Babadağ, K. (2006). Zeki Veri Madenciliği: Ham Veriden Altın Bilgiye Ulaşma Yöntemleri. Industrial Application Software, 85-87
• Jacobs, P. (1999). Data Mining: What General Managers Need To Know. Harvard Management Update, 4 (10): 8.
• Alataş, B. ve Akın, E. (2004). Veri Madenciliğinde Yeni Yaklaşımlar. Ya/Em-2004- Yöneylem Araştırması/Endüstri Mühendisliği XXIV Ulusal Kongresi, 15-18 Haziran, Gaziantep-Adana.
• Fayyad, U.M., Piatetsky-Shapiro, G. and Smyth, P. (1996). From Data Mining to Knowledge Discovery: An Overview. AKDDM, AAAI/MIT Press, pp. 1-30.
• Bırtıl, F. S. (2011). Kız Meslek Lisesi Öğrencilerinin Akademik Başarısızlık Nedenlerinin Veri Madenciliği Tekniği İle Analizi. Afyon Kocatepe Üniversitesi Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi
• Ministry of Health. (2015). Türkiye Halk Sağlığı Kurumu, Türkiye Kalp ve Damar Hastalıkları Önleme ve Kontrol Programı Eylem Planı 2015-2020, Ankara 2015.
• WHO. (2014). Global Status Report on Noncommunicable Diseases. https://www.who.int/nmh/publications/ncd-status-report-2014/en/ (Erişim tarihi: Temmuz 2019)
• Kültürsay, H. (2011). Kardiyovasküler Hastalık Riski Hesaplama Yöntemleri. Türk Kardiyol Dern Arş - Arch Turk Soc Cardiol 2011, 39 Suppl 4:6-13 doi: 10.5543/tkda.2011.kultursay
• TUİK Sayı: 27620. (Erişim tarihi: Temmuz 2019) http://www.tuik.gov.tr/PreHaberBultenleri.do?id=27620
• Tekkeşin, N., Kılınç, C. ve Ökmen, A.Ş. (2011). Türk Erişkinlerde Framingham Risk Faktörlerinin Araştırılması. Klinik ve Deneysel Araştırmalar Dergisi / 2011; 2 (1): 42-49
• Framingham Risk Skorlaması. (2020). http://www.hipertansiyonmd.com/hesaplama/hesaplama_Framingham_risk_skorlamasi.htm (Erişim Tarihi: Şubat 2020)
• Peter, W. F. W., Ralph, B. D., Daniel L., Albert M. B., Halit S. ve William B. K. (1998). Prediction of Coronary Heart Disease Using Risk Factor Categories, Circulation, Vol. 97, No. 18, 12 May 1998, 1998;97:1837–1847 https://doi.org/10.1161/01.CIR.97.18.1837.
• Kitiş, Ş. “Framingham Risk Score By Data Mining Method“, 9th International Conference on Advanced Technologies 2020, Online İstanbul, 10-12 August 2020, ISBN:978-625-44427-0-4.