Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Bibliografische gegevens
Hoofdauteurs:	Sakib, Hasibul, Alok, Aditto Baidya, Huq, Fardin, Ullah, Shamsil Arafin, Ghosh, Riya
Andere auteurs:	Shakil, Arif
Formaat:	Thesis
Taal:	English
Gepubliceerd in:	Brac University 2024
Onderwerpen:	SEER Data Machine learning Deep Learning Cancer Survivability Machine learning. Data mining. Cancer > Chemotherapy. Cancer > Radiotherapy.
Online toegang:	http://hdl.handle.net/10361/22713

id	10361-22713
record_format	dspace
spelling	10361-227132024-05-05T21:05:35Z Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning Sakib, Hasibul Alok, Aditto Baidya Huq, Fardin Ullah, Shamsil Arafin Ghosh, Riya Shakil, Arif Department of Computer Science and Engineering, Brac University SEER Data Machine learning Deep Learning Cancer Survivability Machine learning. Data mining. Cancer--Chemotherapy. Cancer--Radiotherapy. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis Includes bibliographical references (page 81-84). Cancer, an enduring medical enigma with historical recognition dating back to ancient civilizations, remains without a definitive cure. This research undertakes a comprehensive investigation encompassing nine prevalent global cancer types, including those with significant implications for the population of Bangladesh. Employing cutting-edge machine learning (ML) and deep learning (DL) models, as well as traditional machine learning techniques, our study derives its strength from an extensive dataset sourced from the Surveillance, Epidemiology, and End Results (SEER) program. Our research endeavors to unravel the intricate tapestry of cancer by distilling pivotal insights from substantial datasets. At its core, our mission is to redefine the landscape of cancer treatment through the creation of predictive models, thus heralding an era of personalized and highly efficacious cancer therapies. Based on a hypothesis, our objective seeks to improve cancer treatment by developing predictive models. Through a comparative analysis involving traditional machine learning models, deep learning algorithms, and boosting models, we have discovered that the boosting models stand out in terms of accuracy, indicating their potential to enhance predictive precision for therapeutic response. We hypothesize that the surgical removal of localized tumors can effectively arrest cancer progression, thereby increasing patient survival. This encapsulates the main focus of our study, which is a committed attempt to identify unique answers to a persistent medical dilemma by integrating the knowledge of the past with the potential of the future. Hasibul Sakib Aditto Baidya Alok Fardin Huq Shamsil Arafin Ullah Riya Ghosh B.Sc. in Computer Science 2024-05-05T04:13:32Z 2024-05-05T04:13:32Z 2023 2023-09 Thesis ID 19101283 ID 19101509 ID 22241141 ID 19101164 ID 19101327 http://hdl.handle.net/10361/22713 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 96 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	SEER Data Machine learning Deep Learning Cancer Survivability Machine learning. Data mining. Cancer--Chemotherapy. Cancer--Radiotherapy.
spellingShingle	SEER Data Machine learning Deep Learning Cancer Survivability Machine learning. Data mining. Cancer--Chemotherapy. Cancer--Radiotherapy. Sakib, Hasibul Alok, Aditto Baidya Huq, Fardin Ullah, Shamsil Arafin Ghosh, Riya Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Shakil, Arif
author_facet	Shakil, Arif Sakib, Hasibul Alok, Aditto Baidya Huq, Fardin Ullah, Shamsil Arafin Ghosh, Riya
format	Thesis
author	Sakib, Hasibul Alok, Aditto Baidya Huq, Fardin Ullah, Shamsil Arafin Ghosh, Riya
author_sort	Sakib, Hasibul
title	Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning
title_short	Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning
title_full	Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning
title_fullStr	Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning
title_full_unstemmed	Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning
title_sort	hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22713
work_keys_str_mv	AT sakibhasibul hypothesizingprecisecancertreatmentsbasedonpatientsurvivalusingmachinelearningdeeplearning AT alokadittobaidya hypothesizingprecisecancertreatmentsbasedonpatientsurvivalusingmachinelearningdeeplearning AT huqfardin hypothesizingprecisecancertreatmentsbasedonpatientsurvivalusingmachinelearningdeeplearning AT ullahshamsilarafin hypothesizingprecisecancertreatmentsbasedonpatientsurvivalusingmachinelearningdeeplearning AT ghoshriya hypothesizingprecisecancertreatmentsbasedonpatientsurvivalusingmachinelearningdeeplearning
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Hypothesizing precise cancer treatments based on patient survival using machine learning & deep learning

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