Diagnosis of Breast Cancer using Decision Tree Data Mining Technique

Sumbaly, Ronak; Vishnusri, N.; Jeyalatha, S.

doi:10.5120/17219-7456

Cited by 70 publications

(32 citation statements)

References 5 publications

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“…In 2014, a J48 decision tree method, originally developed by the WEKA project team [96], was used to classify WBCD with a classification accuracy of 94.56% [97]. In 2016, an accuracy of 99.9% was achieved using three steps [98]: clustering using farthest first clustering (FFC), pre-processing using outlier detection (OD) and classification using the J48 decision tree.…”

Section: Dtsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

213

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Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

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Section: Dtsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

213

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“…Iheme et al [26] used a naïve Bayesian classifier and a decision-stump-tree classifier to create a decision-making system to support child diagnostics in rural areas in India. Sumbaly et al [27] used decision trees for cancer diagnosis and Kumar et al [28] to analyze data from road traffic accidents.…”

Section: Use Of Decision Treesmentioning

confidence: 99%

Analysis of the Impact of Selected Physical Environmental Factors on the Health of Employees: Creating a Classification Model Using a Decision Tree

Andrejiová

Piňosová

Kralikova

et al. 2019

IJERPH

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During the process of designing and implementing a working environment, there is a need to guarantee adequate conditions for future workers' health and well-being. This article addresses the classification of employees characterized by several basic input variables (gender, age, class of work). The investigated variable was the health of employees. This article aims to create a prediction classification model using the classification tree, which can be used to classify new cases into appropriate classes as accurately as possible. Objective measurements of microclimatic parameters were performed by the Testo 435 instrument. The subjective evaluation was performed by a questionnaire survey formed from the training group of 80 respondents and independently verified by the test group of 80 more respondents. The result confusion matrix shows that the number of correctly classified respondents was 69 from a total of 80 respondents. The overall accuracy was A C = 0.863, which means that the likelihood that respondents are properly classified in the correct health class is 86.3%. Based on the model obtained using the classification tree, we can classify respondents into the relevant class for their state of health. The respondent is classified into the class of work for which particular health and working conditions are most likely.

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“…Sumbaly et al [7] have discussed j48 decision tree classification algorithm for breast cancer diagnosis along with the summarization on the types of breast cancer, risk factors, disease symptoms and treatment. The authors have proven that the j48 algorithm is able to produce 94.5% of accuracy with correctly classified instances and have also suggested that neural network and digital mammography would be the alternative approaches for breast cancer prediction.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Novel Iterative Linear Regression Perceptron Classifier for Breast Cancer Prediction

Durai¹,

Ganesh²

2017

IJCA

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Breast cancer, the most common of types of cancer that threatens human life more specifically women can be diagnosed with classification techniques of data mining. This work is an extension of earlier implementation of breast cancer analysis of the author through iterative linear regressive classifier. The objective of this study is to make cent percent prediction accuracy in the diagnosis of breast cancer over the traditional Wisconsin dataset. The novelty of the paper includes the benefits of the previous ILRC and also takes the advantages of AI. The results of the proposed work are evaluated against the randmeasure and have proven that the results yield cent percent prediction accuracy in diagnosing breast cancer.

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Diagnosis of Breast Cancer using Decision Tree Data Mining Technique

Cited by 70 publications

References 5 publications

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Analysis of the Impact of Selected Physical Environmental Factors on the Health of Employees: Creating a Classification Model Using a Decision Tree

A Novel Iterative Linear Regression Perceptron Classifier for Breast Cancer Prediction

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