Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients

Today, cancer is the second leading cause of death worldwide, and the number of people diagnosed with the disease is expected to rise. Breast cancer is the most commonly diagnosed cancer in women, and it has one of the highest survival rates when treated properly. Because the effectiveness and, as a...

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Autores principales:	Roqia Saleem Awad Maabreh, Malik Bader Alazzam, Ahmed S. AlGhamdi
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Biotechnology TP248.13-248.65 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f02fe6f9d9ac4145b40d8b6419a2792c
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spelling	oai:doaj.org-article:f02fe6f9d9ac4145b40d8b6419a2792c2021-11-29T00:56:18ZMachine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients1754-210310.1155/2021/9338091https://doaj.org/article/f02fe6f9d9ac4145b40d8b6419a2792c2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9338091https://doaj.org/toc/1754-2103Today, cancer is the second leading cause of death worldwide, and the number of people diagnosed with the disease is expected to rise. Breast cancer is the most commonly diagnosed cancer in women, and it has one of the highest survival rates when treated properly. Because the effectiveness and, as a result, survival of the patient are dependent on each case, it is critical to know the modelling of their survival ahead of time. Artificial intelligence is a rapidly expanding field, and its clinical applications are following suit (having surpassed humans in many evidence-based medical tasks). From the inception of since first stable risk estimator based on statistical methods appeared in survival analysis, there have been numerous versions of it created, with machine learning being used in only a few of them. Nonlinear relationships between variables and the impact they have on the variable to be predicted are very easy to evaluate using statistical methods. However, because they are just mathematical equations, they have flaws that limit the quality of their output. The main goal of this study is to find the best machine learning algorithms for predicting the individualised survival of breast cancer patients, as well as the most appropriate treatment, and to propose new numerical variable stratifications. They will still be carried out using unsupervised machine learning methods that divide patients into groups based on their risk in each dataset. We will compare it to standard groupings to see if it has more significance. Knowing that the greatest challenge in dealing with clinical data is its quantity and quality, we have gone to great lengths to ensure their quality before replicating them. We used the Cox statistical method in conjunction with other statistical methods and tests to find the best possible dataset with which to train our model, despite its ease of multivariate analysis.Roqia Saleem Awad MaabrehMalik Bader AlazzamAhmed S. AlGhamdiHindawi LimitedarticleBiotechnologyTP248.13-248.65Biology (General)QH301-705.5ENApplied Bionics and Biomechanics, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biotechnology TP248.13-248.65 Biology (General) QH301-705.5
spellingShingle	Biotechnology TP248.13-248.65 Biology (General) QH301-705.5 Roqia Saleem Awad Maabreh Malik Bader Alazzam Ahmed S. AlGhamdi Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients
description	Today, cancer is the second leading cause of death worldwide, and the number of people diagnosed with the disease is expected to rise. Breast cancer is the most commonly diagnosed cancer in women, and it has one of the highest survival rates when treated properly. Because the effectiveness and, as a result, survival of the patient are dependent on each case, it is critical to know the modelling of their survival ahead of time. Artificial intelligence is a rapidly expanding field, and its clinical applications are following suit (having surpassed humans in many evidence-based medical tasks). From the inception of since first stable risk estimator based on statistical methods appeared in survival analysis, there have been numerous versions of it created, with machine learning being used in only a few of them. Nonlinear relationships between variables and the impact they have on the variable to be predicted are very easy to evaluate using statistical methods. However, because they are just mathematical equations, they have flaws that limit the quality of their output. The main goal of this study is to find the best machine learning algorithms for predicting the individualised survival of breast cancer patients, as well as the most appropriate treatment, and to propose new numerical variable stratifications. They will still be carried out using unsupervised machine learning methods that divide patients into groups based on their risk in each dataset. We will compare it to standard groupings to see if it has more significance. Knowing that the greatest challenge in dealing with clinical data is its quantity and quality, we have gone to great lengths to ensure their quality before replicating them. We used the Cox statistical method in conjunction with other statistical methods and tests to find the best possible dataset with which to train our model, despite its ease of multivariate analysis.
format	article
author	Roqia Saleem Awad Maabreh Malik Bader Alazzam Ahmed S. AlGhamdi
author_facet	Roqia Saleem Awad Maabreh Malik Bader Alazzam Ahmed S. AlGhamdi
author_sort	Roqia Saleem Awad Maabreh
title	Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients
title_short	Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients
title_full	Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients
title_fullStr	Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients
title_full_unstemmed	Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients
title_sort	machine learning algorithms for prediction of survival curves in breast cancer patients
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/f02fe6f9d9ac4145b40d8b6419a2792c
work_keys_str_mv	AT roqiasaleemawadmaabreh machinelearningalgorithmsforpredictionofsurvivalcurvesinbreastcancerpatients AT malikbaderalazzam machinelearningalgorithmsforpredictionofsurvivalcurvesinbreastcancerpatients AT ahmedsalghamdi machinelearningalgorithmsforpredictionofsurvivalcurvesinbreastcancerpatients
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Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients

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