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Journals / İstanbul Ticaret Üniversitesi Teknoloji ve Uygulamalı Bilimler Dergisi / 2020 / Cilt: 3 - Sayı: 1

ENHANCING DETECTION METHOD OF BREAST CANCER USING COIMBRA DATASET

Adil Hani ABDULKAREEM
Mustafa Cem KASAPBAŞI
Pages
51–59
Publication Date
2020-08-30
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings provide promising evidence that models combining resistin, glucose, age, and BMI may be a powerful tool for breast cancer detection.

📌 3 Key Takeaways

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Objective & Hypothesis:Breast cancer is one of the most dangerous and second most common types of cancer in the world.
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Key Findings & Data:From the UC Irvine Machine Learning Repository (UCI) library Coimbra Breast Cancer data set which consists of age, glucose, body mass index (BMI), resistin, insulin, adiponectin, homeostatic model assessment (HOMA), monocyte chemoattractant protein-1 (MCP1), and leptin attributes were used.
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Conclusion & Impact:These findings provide promising evidence that models combining resistin, glucose, age, and BMI may be a powerful tool for breast cancer detection.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Coimbra dataset · Cancer biomarker · Data mining

Abstract

Breast cancer is one of the most dangerous and second most common types of cancer in the world. Breast cancerfighting with developed devices and medical therapies has become easier. To obtain the best result in breast cancer treatment, periodic checks should be carried out to follow the early diagnosis. Data Mining techniques are used to predict the success of treatment or diagnosis. In this study, the K-Nearest Neighbor (k-NN), Naïve Bayes classifier algorithms of machine learning were used for early detection of breast cancer. From the UC Irvine Machine Learning Repository (UCI) library Coimbra Breast Cancer data set which consists of age, glucose, body mass index (BMI), resistin, insulin, adiponectin, homeostatic model assessment (HOMA), monocyte chemoattractant protein-1 (MCP1), and leptin attributes were used. K-NN model using Age, Resistin, Glucose, and BMI give the highest results, where 90% of specificity 84% percent of sensitivity, and 87.5% accuracy is achieved. These findings provide promising evidence that models combining resistin, glucose, age, and BMI may be a powerful tool for breast cancer detection.

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Cite This Article

ABDULKAREEM, A. H., KASAPBAŞI, M. C. (2020). ENHANCING DETECTION METHOD OF BREAST CANCER USING COIMBRA DATASET. İstanbul Ticaret Üniversitesi Teknoloji ve Uygulamalı Bilimler Dergisi, Cilt: 3 - Sayı: 1, 51–59. https://ajindex.com/articles/373364

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References (13)

  1. Annasaheb, A. B., & Verma, V. K. (2016). Data Mining Classification Techniques: A Recent. International Journal of Emerging Technologies in Engineering Research, 51-54. Araújo, V. J., Guimarães, A. J., Souza, P. V., Rezende, T. S., & Araújo, V. S. (2019).
  2. Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer. Machine Learning and Knowledge Extraction, 466-482.
  3. Aslan, M. F., Celik, Y., Sabanci, K., & Durdu, A. (2018). Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data. International Journal of Intelligent Systems and Applications in Engineering, 289-293.
  4. Brabletz, T., Jung, A., Spaderna, S., Hlubek, F., & Kirchner, T. (2005). Migrating cancer stem cells — an integrated concept of malignant tumour progression. Nat Rev Cancer, 744-749.
  5. Gültepe, Y., & Kartbayev, T. (2019). A Study of Data Mining Methods for Breast Cancer Prediction. Proceedings Book, 303-305.
  6. Han, J., Kamber, M., & Pei, J. (2006). Data Mining: Concepts And Techniques. San Francisco: University of Illinois. Imaginis. (n.d.). Retrieved March 2019, from http://www.imaginis.com/general-information-on-breastcancer/what-is-breast-cancer-2
  7. Li, Y., & Chen, Z. (2018). Performance Evaluation of Machine Learning Methods for Breast Cancer Prediction. Applied and Computational Mathematics, 212-216.
  8. Özkan, Y. (2016). Veri Madenciliği Yöntemleri. Istanbul: Papatya Yayıncılık Eğitim. Patrício, M., Pereira, J., Crisóstomo, J., Matafome, P., Gomes, M., Seiça, R., et al. (2018). Using Resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer, 1-8.
  9. Poorani, S., & Balasubramanie, P. (2019). Deep Neural Network Classifier in Breast Cancer Prediction. International Journal of Engineering and Advanced Technology, 2106-2109.
  10. Purwaningsih, E. (2019). Application of the Support Vector Machine and Neural Network Model Based on Particle Swarm Optimization for Breast Cancer Prediction. SinkrOn, 66-73.
  11. S.B.Akben. (2019).
  12. Determination of the Blood, Hormone and Obesity Value Ranges that Indicate the Breast Cancer, Using Data Mining Based Expert System. Innovation and Research in BioMedical engineering IRBM, 355–360.
  13. Sammut, C., & Webb, G. I. (2017). Encyclopedia of Machine Learning and Data Mining. Springer Publishing Company, Incorporated, 2nd edition. Society, A. C. (2020). Breast Cancer Facts & Figures 2019-2020. A Cancer Journal for Clinicians. Soliman, O. S., & AboElHa