Decision Tree Classification and Model Evaluation for Breast Cancer Survivability: A Data Mining Approach

Ponnuraja, C; Lakshmanan, Babu C; Valarmathi, S; B, Krihsna Prasanth

doi:10.13005/bpj/1107

Cited by 11 publications

(5 citation statements)

References 6 publications

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“…Breast cancer appears to be the most common cancer type suffered by women across the globe, which stands after lung cancer amidst developed nations [ 1 – 3 ]. In Malaysia, 50–60% of breast cancer cases are detected at late stages, hence the survival of the patients is one of the lowest in the region [ 4 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting factors for survival of breast cancer patients using machine learning techniques

Ganggayah

Taib

Har

et al. 2019

BMC Med Inform Decis Mak

204

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BackgroundBreast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate.MethodsA large hospital-based breast cancer dataset retrieved from the University Malaya Medical Centre, Kuala Lumpur, Malaysia (n = 8066) with diagnosis information between 1993 and 2016 was used in this study. The dataset contained 23 predictor variables and one dependent variable, which referred to the survival status of the patients (alive or dead). In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and support vector machine. Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest. Subsequently, the important variables were ranked via variable selection methods in random forest. Finally, decision trees were built and validation was performed using survival analysis.ResultsIn terms of both model accuracy and calibration measure, all algorithms produced close outcomes, with the lowest obtained from decision tree (accuracy = 79.8%) and the highest from random forest (accuracy = 82.7%). The important variables identified in this study were cancer stage classification, tumour size, number of total axillary lymph nodes removed, number of positive lymph nodes, types of primary treatment, and methods of diagnosis.ConclusionInterestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival studies, particularly in the Asian region. The important prognostic factors influencing survival rate of breast cancer identified in this study, which were validated by survival curves, are useful and could be translated into decision support tools in the medical domain.

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Section: Introductionmentioning

confidence: 99%

Predicting factors for survival of breast cancer patients using machine learning techniques

Ganggayah

Taib

Har

et al. 2019

BMC Med Inform Decis Mak

204

View full text Add to dashboard Cite

show abstract

“…Protein sequence prediction and analysis are performed using a hybrid Knuth-Morris Pratt (KMP) and Boyer-Moore (BM) method 28 . Decision Tree based model evaluation is performed for breast cancer dataset using data mining approaches 29 . The Particle Swarm Optimization (PSO) algorithm was used to identify the cancer specific gene selection 30 .…”

Section: Literature Reviewmentioning

confidence: 99%

“…Based on the existing literatures, this research work focused on classifying the SEER breast cancer dataset using Machine Learning models such as Supervised and Ensemble Learning. In the exiting literature 29 , the features were chosen according to previously published sources and the features were chosen at random that were influenced by clinical and statistical significance. The current work focuses primarily on the features that were chosen from the SEER dataset using advanced feature selection techniques like Variance Threshold and PCA methods.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrative machine learning framework for classifying SEER breast cancer

Manikandan¹,

Durga²,

Ponnuraja

2023

Sci Rep

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Breast cancer is the commonest type of cancer in women worldwide and the leading cause of mortality for females. The aim of this research is to classify the alive and death status of breast cancer patients using the Surveillance, Epidemiology, and End Results dataset. Due to its capacity to handle enormous data sets systematically, machine learning and deep learning has been widely employed in biomedical research to answer diverse classification difficulties. Pre-processing the data enables its visualization and analysis for use in making important decisions. This research presents a feasible machine learning-based approach for categorizing SEER breast cancer dataset. Moreover, a two-step feature selection method based on Variance Threshold and Principal Component Analysis was employed to select the features from the SEER breast cancer dataset. After selecting the features, the classification of the breast cancer dataset is carried out using Supervised and Ensemble learning techniques such as Ada Boosting, XG Boosting, Gradient Boosting, Naive Bayes and Decision Tree. Utilizing the train-test split and k-fold cross-validation approaches, the performance of various machine learning algorithms is examined. The accuracy of Decision Tree for both train-test split and cross validation achieved as 98%. In this study, it is observed that the Decision Tree algorithm outperforms other supervised and ensemble learning approaches for the SEER Breast Cancer dataset.

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“…e work from Ref. [6] establishes a DT-based prediction model by separating cancer patients into different groups based on their age and gender. Additionally, their results also identify two high-risk groups, that is, the female group with the age between 42 and 52 and the male group with their ages less than 42. ose findings can be further used to assist clinicians with useful guidance to provide better individual treatment plan.…”

Section: Existing Prediction Modelsmentioning

confidence: 99%

Sparse-Coding-Based Autoencoder and Its Application for Cancer Survivability Prediction

Huang

Wang

Zhang

2022

Mathematical Problems in Engineering

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Cancer-survivability prediction is one of the popular research topics, that attracted great attention from both the health service providers and academia. However, one remaining question comes from how to make full use of a large number of available factors (or features). This paper, accordingly, presents a novel autoencoder algorithm based on the concept of sparse coding to address this problem. The main contribution is twofold: the utilization of sparsity coding for input feature selection and a subsequent classification using latent information. Precisely, a typical autoencoder architecture is employed for reconstructing the original input. Then the sparse coding technique is applied to optimize the network structure, with the aim of selecting optimal features and enhancing the generalization capability. In addition, the refined latent information is further cast as alternative features for training a sparse classifier. To evaluate the performance of the proposed autoencoder architecture, we present a comprehensive analysis using a publicly available data repository (i.e., Surveillance, Epidemiology, and End Results, SEER). Experimental study shows that the proposed approach has the ability of extracting important features from high-dimensional inputs and achieves competitive performance than other state-of-the-art classification techniques.

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Decision Tree Classification and Model Evaluation for Breast Cancer Survivability: A Data Mining Approach

Cited by 11 publications

References 6 publications

Predicting factors for survival of breast cancer patients using machine learning techniques

Predicting factors for survival of breast cancer patients using machine learning techniques

An integrative machine learning framework for classifying SEER breast cancer

Sparse-Coding-Based Autoencoder and Its Application for Cancer Survivability Prediction

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