Survival analysis of breast cancer patients using machine learning models

I., Keren Evangeline; Kirubha, S. P. Angeline; Precious, J. Glory

doi:10.1007/s11042-023-14989-8

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…Evangeline et al [4] used three different models-namely, the Cox Proportional Hazards (CoxPH) model, the Random Survival Forests (RSF) model, and DeepHit-in their study for the survival prediction of breast cancer patients. As a result of these experimental studies, successful results were obtained in the RFS and DeepHit models.…”

Section: Literature Reviewmentioning

confidence: 99%

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Bülbül,

Işık

2024

Biomimetics

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The prediction of patient survival is crucial for guiding the treatment process in healthcare. Healthcare professionals rely on analyzing patients’ clinical characteristics and findings to determine treatment plans, making accurate predictions essential for efficient resource utilization and optimal patient support during recovery. In this study, a hybrid architecture combining Stacked AutoEncoders, Particle Swarm Optimization, and the Softmax Classifier was developed for predicting patient survival. The architecture was evaluated using the Haberman’s Survival dataset and the Echocardiogram dataset from UCI. The results were compared with several Machine Learning methods, including Decision Trees, K-Nearest Neighbors, Support Vector Machines, Neural Networks, Gradient Boosting, and Gradient Bagging applied to the same datasets. The findings indicate that the proposed architecture outperforms other Machine Learning methods in predicting patient survival for both datasets and surpasses the results reported in the literature for the Haberman’s Survival dataset. In the light of the findings obtained, the models obtained with the proposed architecture can be used as a decision support system in determining patient care and applied methods.

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Section: Literature Reviewmentioning

confidence: 99%

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Bülbül,

Işık

2024

Biomimetics

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Survival analysis in breast cancer: evaluating ensemble learning techniques for prediction

Buyrukoğlu

2024

PeerJ Computer Science

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Breast cancer is most commonly faced with form of cancer amongst women worldwide. In spite of the fact that the breast cancer research and awareness have gained considerable momentum, there is still no one treatment due to disease heterogeneity. Survival data may be of specific interest in breast cancer studies to understand its dynamic and complex trajectories. This study copes with the most important covariates affecting the disease progression. The study utilizes the German Breast Cancer Study Group 2 (GBSG2) and the Molecular Taxonomy of Breast Cancer International Consortium dataset (METABRIC) datasets. In both datasets, interests lie in relapse of the disease and the time when the relapse happens. The three models, namely the Cox proportional hazards (PH) model, random survival forest (RSF) and conditional inference forest (Cforest) were employed to analyse the breast cancer datasets. The goal of this study is to apply these methods in prediction of breast cancer progression and compare their performances based on two different estimation methods: the bootstrap estimation and the bootstrap .632 estimation. The model performance was evaluated in concordance index (C-index) and prediction error curves (pec) for discrimination. The Cox PH model has a lower C-index and bigger prediction error compared to the RSF and the Cforest approach for both datasets. The analysis results of GBSG2 and METABRIC datasets reveal that the RSF and the Cforest algorithms provide non-parametric alternatives to Cox PH model for estimation of the survival probability of breast cancer patients.

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Survival analysis of breast cancer patients using machine learning models

Cited by 2 publications

References 42 publications

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Survival analysis in breast cancer: evaluating ensemble learning techniques for prediction

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