Browsing by Author "Hossen, Shazzad"

Now showing 1 - 4 of 4

Five-Year Life Expectancy Prediction of Prostate Cancer Patients Using Machine Learning Algorithms
(Springer Nature Limited, 2022-03-08) Polash, Md Shohidul Islam; Hossen, Shazzad; Haque, Aminul
Prostate cancer is the most frequent malignancy and the leading cause of cancer-related mortality globally. A precise survival estimate is required for the effectiveness of treatment to minimize mortality rate. A remedial strategy can be planned under the anticipated survival state. Machine Learning (ML) methods have recently garnered considerable interest, particularly in developing data-driven prediction models. Unfortunately prostate cancer has received less attention to such studies. In this paper, we have built models using machine learning methods to predict whether a patient with prostate cancer would live for five years or not. Compared to prior studies, correlation analysis, a substantial quantity of data, and a unique track with hyperparameter adjustment boost the performance of our model. The SEER(Surveillance, Epidemiology, and End Results) database provided the data for developing these models. The SEER program gathers and disseminates cancer data to mitigate the disease’s effect. We analyzed prostate cancer patients’ five-year survival state using about seven prediction models. Gradient Boosting, Light Gradient Boosting Machine, and Ada Boost algorithms are identified as top-performed prediction models. Among them, a tuned prediction model using the Gradient Boosting algorithm outperforms others, with an accuracy of 88.45% and found fastest among the other models.
Model Analysis for Predicting Prostate Cancer Patient’s Survival: A SEER Case Study
(Springer Nature, 2023-05-28) Polash, Md. Shohidul Islam; Hossen, Shazzad; Haque, Aminul
Prostate cancer is assumed to be the most familiar cancer and the principal cause of death in the world. For effective treatment to decrease mortality, an accurate survival projection is essential. A remedy plan can be scheme under the predicted survival state. Machine Learning (ML) approaches have recently attracted significant attention, particularly in constructing data-driven prediction models. Prostate cancer survival prediction has received little attention in research. In this article, we constructed models with the support of ML techniques to determine the possibility of whether a patient with prostate cancer will survive or not. Feature impact analysis, a good amount of data, and a distinctive track make our model’s results better compared to previous research. The models have created using data from the SEER (Surveillance, Epidemiology, End Results) database. SEER program collects and distributes cancer statistics to lessen the disease impact. Using around twelve prediction models, we assessed the survival of prostate cancer patients. HGB, LGBM, XGBoost, Gradient Boosting, and Ada Boost are notable prediction models. Among them, the XGBoost is the best contribution, with an accuracy of 89.57%, and found to be faster among the models.
Survival Analysis of Thyroid Cancer Patients Using Machine Learning Algorithms
(IEEE, 2024-04-22) Alhashmi, Saadat M.; Polash, Md. Shohidul Islam; Haque, Aminul; Rabbe, Fazley; Hossen, Shazzad; Faruqui, Nuruzzaman; Hashem, Ibrahim Abaker Targio; Abubacker, Nirase Fathima
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% 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.
Survival Analysis of Thyroid Cancer Patients Using Machine Learning Algorithms
(Scopus, 2024-04-22) Alhashmi, Saadat M.; Polash, Md. Shohidul Islam; Haque, Aminul; Rabbe, Fazley; Hossen, Shazzad; Faruqui, Nuruzzaman
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% 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.