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Research Article Open access CC BY 4.0

A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

Shler Farhad Khorshid, Adnan Mohsin Abdulazeez, Amira Bibo Sallow

Asian Journal of Research in Computer Science · pp. 45–59 · Published 19 May 2021

10.9734/ajrcos/2021/v8i430209

Abstract

Breast cancer is one of the most common diseases among women, accounting for many deaths each year. Even though cancer can be treated and cured in its early stages, many patients are diagnosed at a late stage. Data mining is the method of finding or extracting information from massive databases or datasets, and it is a field of computer science with a lot of potentials. It covers a wide range of areas, one of which is classification. Classification may also be accomplished using a variety of methods or algorithms. With the aid of MATLAB, five classification algorithms were compared. This paper presents a performance comparison among the classifiers: Support Vector Machine (SVM), Logistics Regression (LR), K-Nearest Neighbors (K-NN), Weighted K-Nearest Neighbors (Weighted K-NN), and Gaussian Naïve Bayes (Gaussian NB). The data set was taken from UCI Machine learning Repository. The main objective of this study is to classify breast cancer women using the application of machine learning algorithms based on their accuracy. The results have revealed that Weighted K-NN (96.7%) has the highest accuracy among all the classifiers.

Breast cancer data mining SVM Logistics regression weighted K-NN Gaussian Naïve Bayes

References (41)

  1. 1 An Introduction to Statistical Learning [DOI]
  2. 2 Data mining [DOI]
  3. 3 Classification Based on Decision Tree Algorithm for Machine Learning [DOI]
  4. 4 A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction [DOI]
  5. 5 Handbook of Statistical Analysis and Data Mining Applications [DOI]
  6. 6 A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems [DOI]
  7. 7 Particle Swarm Optimization Feature Selection for Breast Cancer Recurrence Prediction [DOI]
  8. 8 A Note on Distance-Weighted k-Nearest Neighbor Rules [DOI]
  9. 9 A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications [DOI]
  10. 10 Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images [DOI]
  11. 11 DATA MINING CLASSIFICATION TECHNIQUES APPLIED FOR BREAST CANCER DIAGNOSIS AND PROGNOSIS
  12. 12 Gene Selection and Classification of Microarray Data Using Convolutional Neural Network [DOI]
  13. 13 Machine learning and Region Growing for Breast Cancer Segmentation [DOI]
  14. 14 Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis [DOI]
  15. 15 A Comparative Analysis of Nonlinear Machine Learning Algorithms for Breast Cancer Detection [DOI]
  16. 16 Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer [DOI]
  17. 17 Gene Expression Classification Based on Deep Learning [DOI]
  18. 18 Breast Cancer Prediction and Detection Using Data Mining Classification Algorithms: A Comparative Study [DOI]
  19. 19 Multi-Level Fusion in Ultrasound for Cancer Detection based on Uniform LBP Features [DOI]
  20. 20 Evaluating Data Mining Classification Methods Performance in Internet of Things Applications
  21. 21 Breast Cancer Prediction Applying Different Classification Algorithm with Comparative Analysis using WEKA [DOI]
  22. 22 Comparison of Classification Models for Early Prediction of Breast Cancer [DOI]
  23. 23 A Review on Data mining from Past to the Future [DOI]
  24. 24 Data mining and medical world: breast cancers' diagnosis, treatment, prognosis and challenges.
  25. 25 Application of Data Mining Techniques for Medical Data Classification: A Review [DOI]
  26. 26 Using Data Mining Tools for Breast Cancer Prediction and Analysis [DOI]
  27. 27 Data Mining Classification Algorithms for Analyzing Soil Data [DOI]
  28. 28 On Predicting and Analyzing Breast Cancer using Data Mining Approach [DOI]
  29. 29 Using Twitter for Tapping Public Minds, Predict Trends and Generate Value [DOI]
  30. 30 Gene clustering with partition around mediods algorithm based on weighted and normalized mahalanobis distance [DOI]
  31. 31 Evaluation of Breast Cancer Tumor Classification with Unconstrained Functional Networks Classifier [DOI]
  32. 32 Using Feature Selection Techniques to Improve the Accuracy of Breast Cancer Classification [DOI]
  33. 33 A DIDS Based on The Combination of Cuttlefish Algorithm and Decision Tree [DOI]
  34. 34 Bearing Fault Diagnosis Using Empirical Mode Decomposition, Entropy Based Features And Data Mining Techniques [DOI]
  35. 35 A Comparative Study of a New Hand Recognition Model Based on Line of Features and Other Techniques [DOI]
  36. 36 Improved Framework for Breast Cancer Prediction Using Frequent Itemsets Mining for Attributes Filtering [DOI]
  37. 37 Study on different classification technique for mammogram image [DOI]
  38. 38 Experimental Studies to Assess Surface Wear Using Grease Degradation, Bearing Temperature and Statistical Parameter of Vibration Signals in a Roller Bearing [DOI]
  39. 39 Research on intelligent fault diagnosis of gears using EMD, spectral features and data mining techniques [DOI]
  40. 40 Classification of Digital Mammograms Using Nearest Neighbor Techniques [DOI]
  41. 41 Survey of Nearest Neighbor Techniques [DOI]

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