Knowledge Mining Approach For Healthy Monitoring From Pregnancy Data With Big Volumes

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 141 - 145, 26.12.2016

Yunus Santur Sinem Güven Santur

https://doi.org/10.18201/ijisae.270369

Cited By: 7

Öz

The process for obtaining information that will
create value on a large-scale data stack is called data mining by its general
name. Data mining is commonly used in sales and marketing departments, in
determining strategies and making critical decisions for the future in many
sectors. Similarly, data mining is used in the determination of health
policies, more effective implementation of health services and in the
management of resources and institutions in the health sector. In this study, it was aimed to create a software
architecture of data mining that will help the personal monitoring of the
pregnancy process in a more effective way in the health sector. Many different types of data such as age, gender, location, education,
physical characteristics, lifestyle habits and medical history of the people
that could be used for this purpose are stored online by health institutions.
The machine learning algorithms have been created to determine classification,
clustering and association rule on these data. 

Anahtar Kelimeler

Data Mining, Knowledge Mining, Healthy Monitoring, Classification, Clustering

Kaynakça

• Savaş S., Topaloğlu N and M. Yılmaz M. Veri madenciliği ve Türkiye’deki uygulama örnekleri, 2012.
• Wu X., Zhu X., Wu G. Q., Ding W. Data mining with big data, IEEE transactions on knowledge and data .eng., 26(1), 97-107, 2014.
• Ünal O.O. Internet Kullanım Analizi Ve Kullanıcı Betimleme Konularında Veri Madenciliği Uygulamaları, Doctoral dissertation, Fen Bilimleri Enstitüsü, 2015.
• Tosun T. Veri Madenciliği Teknikleriyle Kredi Kartlarında Müşteri Kaybetme Analizi, Doctoral dissertation, Fen Bilimleri Enstitüsü, 2015.
• Ocal N., Ercan M. K and E. Kadioglu. Predicting Financial Failure Using Decision Tree Algorithms: An Empirical Test on the Manufacturing Industry at Borsa Istanbul. International Journal of Economics and Finance,7(7), 189, 2015.
• Gürsoy U.T.Ş. Customer Churn Analysis in Telecommunication Sector”, İstanbul University Journal of the School of Business Administration, V.39-1, 35-49, 2010.
• GÜLEN Ö and Özdemir S. Veri Madenciliği Teknikleri İle Üstün Yetenekli Öğrencilerin İlgi Alanlarının Analizi, Üstün Yetenekliler Eğitimi ve Araştırmaları Dergisi (UYAD), 1(3), 2013.
• Kaya M. and Özel S.A. Açık Kaynak Kodlu Veri Madenciliği Yazılımlarının Karşılaştırılması, Akademik Bilişim, 2014.
• Tantuğ A.C. Veri Madenciliğin Ve Demetleme, Doctoral dissertation, Fen Bilimleri Enstitüsü.
• Ertuğrul İ., Organ A. and Şavlı A. The Determination of Patient Profile at Pamukkale Unv. as Relater to the Application of Data Mining, 2013.
• Irmak S., Köksal C. D. and Asilkan Ö. Hastanelerin Gelecekteki Hasta Yoğunluklarının Veri Madenciliği Yöntemleriile Tahmin Edilmesi”. Uluslararası Alanya İşletme Fakültesi Dergisi, 4(1), 2012.
• Yıldız O., Tez M., Bilge H. Ş., Akcayol M. A. and Güler İ. Meme kanseri Sınıflandırması İçin Veri Füzyonu Ve Genetik Algoritma Tabanlı Gen Seçimi, Journal of the Faculty of Engineering & Architecture of Gazi University, 27(3),2012.
• Sharma S., Osei-Bryson K. M. and Kasper G. M. Evaluation of an integrated Knowledge Discovery and Data Mining process model, Expert Systems with Applications, 39(13), 11335-11348, 2012.
• Braha D. Data mining for design and manufacturing: methods and applications, (Vol. 3). Springer Science & Business Media, 2013.
• Cios K.J., Pedrycz W., Swiniarski R. W. Data mining methods for knowledge discovery, 458, Springer Science & Business Media, 2012.
• Larose D.T. Discovering knowledge in data: an introduction to data mining, John Wiley & Sons, 2014.
• Schieve L.A., Cogswell M. E., Scanlon K. S., Perry G., Ferre C., Blackmore-Prince C. Prepregnancy body mass index and pregnancy weight gain: associations with preterm delivery, Obstetrics & Gynecology, 96(2), pp.194-200, NMIHS Coll. Working Group, 2010.
• Witten I.H., Frank E. Data Mining: Practical machine learning tools and techniques”, Morgan Kaufmann, 2005.
• The New Stack (2016). Available: http://thenewstack.io/six-of-the-best-open-source-data-mining-tools.
• Santur S.G., Santur Y. Knowledge Mining Approach For Healthy Monitoring From Pregnancy Data With Big Volumes, International Conference on Advanced Technology & Sciences (ICAT’16) pp. 145-148, 2016.
• Santur Y., Karaköse M., Aydın İ., Akın E. IMU based adaptive blur removal approach using image processing for railway inspection”, In 2016 International Conference on Systems, Signals and Image Processing (IWSSIP’16), pp.1-4, IEEE, 2016.
• Santur Y., Karakose M., Akın E. Random Forest Based Diagnosis Approach for Rail Fault Inspection in Railways", International Conference on Electrical and Electronics Engineering (Eleco’15), 9.th, pp.714-719, 2015.
• Santur Y., Karaköse M., Akın E. Condition Monitoring Approach Using 3d Modelling Of Railway Tracks With Laser Cameras, International Conference on Advanced Technology & Sciences (ICAT’16) pp. 132-135, 2016.