Survival Analysis of Thyroid Cancer Patients Using Machine Learning Algorithms

The medical community strives continually to improve the quality of care patients receive. Predictions of prognosis are essential for doctors and patients to choose a course of treatment. Recent years have witnessed the development of numerous new cancer survival prediction models. Most attempts to...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.61978-61990
Main Authors: Alhashmi, Saadat M., Polash, Md. Shohidul Islam, Haque, Aminul, Rabbe, Fazley, Hossen, Shazzad, Faruqui, Nuruzzaman, Abaker Targio Hashem, Ibrahim, Fathima Abubacker, Nirase
Format: Article
Language:English
Subjects:
Algorithms
Cancer
Classification
Encoding
Epidemiology
Life estimation
Logistic regression
Machine learning
Machine learning algorithms
Medical prognosis
Medical treatment
Model accuracy
Patient monitoring
Prediction models
Predictive models
Prognosis
random forest
Random forests
Survival
Survival analysis
Thyroid cancer
thyroid survivability
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Summary:The medical community strives continually to improve the quality of care patients receive. Predictions of prognosis are essential for doctors and patients to choose a course of treatment. Recent years have witnessed the development of numerous new cancer survival prediction models. Most attempts to predict the prognosis of people with malignant growth rely on classification techniques. We could experiment with significantly different results using only a subset of SEER (Surveillance, Epidemiology, and End Results) data. These models were created using machine learning techniques by selecting univariate features and calculating correlations. We illustrated the variation in results and discrepancy of impurity that can result from varying data quantities and critical factors. Seventeen crucial factors were identified, and a group of classification algorithms were trained to evaluate the effectiveness of an estimation technique. In the display mode, the accuracy of these computations ranges from 97% to 99% \dot{A} long with accuracy, the models are further evaluated regarding the F1 score, precision, recall, and the AUC score. Compared to earlier studies, a more accurate model has been developed, and, to the best of our knowledge, our prediction model is superior to the models studied in the previous works.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3392275