Robust predictive model for evaluating breast cancer survivability

Park, Kanghee; Ali, Amna; Kim, Dokyoon; An, Yeolwoo; Kim, Minkoo; Shin, Hyunjung

doi:10.1016/j.engappai.2013.06.013

Cited by 109 publications

(57 citation statements)

References 28 publications

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“…It is also an ensemble learning method. Unlike adaboosts, which adjust the weights of the samples after each iteration, GBM uses the difference between the last iteration's output and the target value to be the new target for the next iteration [12]. LightGBM is a novel GBM method that implements Gradient-based One-side Sampling (GOSS) and Exclusive Feature Bundling (EFB) techniques.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…Machine learning techniques has been widely applied to predict outcomes for medical purposes [8][9][10][11][12]. Ensemble learning methods that train a number of weak base learners and then combine their outputs are popular in medical prediction researches [22].…”

Section: Related Workmentioning

confidence: 99%

“…The scale of data is large enough to be analyzed. Machine Learning represents a group of methods, including decision trees [8], artificial neural networks (ANN) [9], random forests [10], Adaboost [11], and gradient boost machine [12]. These methods have been widely used to discover a function to represent the relationship between a group variables and an outcome, and have been widely applied to predict cancer patients' outcome.…”

Section: Introductionmentioning

confidence: 99%

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Two Staged Prediction of Gastric Cancer Patient’s Survival Via Machine Learning Techniques

Liu¹,

Li²,

Chen³

et al. 2020

7th International Conference on Artificial Intelligence and Applications

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Cancer is one of the most common causes of death in the world, while gastric cancer has the highest incidence in Asia. Predicting gastric cancer patients' survivability can inform patients care decisions and help doctors prescribe personalized medicine. Classification techniques have been widely used to predict survivability of cancer patients. However, very few attention has been paid to patients who cannot survive. In this research, we consider survival prediction to be a twostaged problem. The first is to predict the patients' five-year survivability. If the patient's predicted outcome is death, the second stage predicts the remaining lifespan of the patient. Our research proposes a custom ensemble method which integrated multiple machine learning algorithms. It exhibits a significant predictive improvement in both stages of prediction, compared with the state-of-the-art machine learning techniques. The base machine learning techniques include Decision Trees, Random Forest, Adaboost, Gradient Boost Machine (GBM), Artificial Neural Network (ANN), and the most popular GBM framework-LightGBM. The model is comprehensively evaluated on open source cancer data provided by the Surveillance, Epidemiology, and End Results Program (SEER) in terms of accuracy, area under the curve, Fscore, precision, recall rate, training and predicting time in the classification stage, and root mean squared error, mean absolute error, coefficient of determination (R 2) in the regression stage.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Two Staged Prediction of Gastric Cancer Patient’s Survival Via Machine Learning Techniques

Liu¹,

Li²,

Chen³

et al. 2020

7th International Conference on Artificial Intelligence and Applications

View full text Add to dashboard Cite

show abstract

“…Our dividing co-training algorithm is designed to efficiently add the most confident unlabeled data to the labeled data pool and it is based on a graph based semi-supervised learning algorithm [27][28]. Compared to the existing algorithms [27][28], our proposed similarity function (Eq. 3) was modified and the weighted data were incorporated into the similarity function.…”

Section: Stage Three: Dividing Co-training Data Labelingmentioning

confidence: 99%

Computerized breast cancer analysis system using three stage semi-supervised learning method

Sun

Tseng

Zhang

et al. 2016

Computer Methods and Programs in Biomedicine

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“…Thus, mortality rate for this cancer has declined in recent years [11]. Using methods that would decrease the maximum memory requirements can also help to utilize predictive systems on smartphones and as a result increase their application performance.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Method for Predicting the 5-year Survivability of Breast Cancer

Jahanbazi¹,

Nadimi²

2016

IJCA

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Breast cancer is one of the most sever type of cancers and is the most common cause of death among the female cancer patients. In order to ease the process of decision making and financial arrangements, it is essential to be aware of survivability of patients. In recent years, effective data-mining techniques have been employed to predict the 5-year survivability of cancer patients, showing reasonable accuracy. The efficiency of these models can be improved by making them accessible on smartphones. In order to achieve this, it is essential to reduce the maximum required memory occupied by the prediction models, since a smartphone has a limited available memory. This issue, which is still an open area of research, is the concern of the present study. A hybrid method is enhanced by combining synthetic minority over-sampling technique (SMOTE), information gain attribute evaluation (InfoGainAttributeEval), AdaBoost.M1 algorithm and a decision tree. The more effective attributes are selected using InfoGainAttributeEval and the less effective nodes are removed by decision tree pre-pruning during the tree building. The hybrid method is further simplified by employing the post-pruning technique on the decision tree after its creation. The proposed method was subjected to a 5-year cancer survivability dataset, showing considerable reduction in the maximum required memory while maintaining the accuracy of prediction.

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Robust predictive model for evaluating breast cancer survivability

Cited by 109 publications

References 28 publications

Two Staged Prediction of Gastric Cancer Patient’s Survival Via Machine Learning Techniques

Two Staged Prediction of Gastric Cancer Patient’s Survival Via Machine Learning Techniques

Computerized breast cancer analysis system using three stage semi-supervised learning method

An Efficient Method for Predicting the 5-year Survivability of Breast Cancer

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