A Research on Machine Learning for Predicting Survival Time in Cancer Patients

Cancer remains one of the leading causes of mortality worldwide. Predicting the survival of cancer patients holds immense significance in guiding treatment decisions and enhancing patient quality of life. To provide a comprehensive understanding of the application of machine learning methods and to explore future research directions in survival prediction, this paper reviews the current landscape of machine learning algorithms in this domain. The paper discusses the significance and challenges associated with predicting the survival time of cancer patients. It begins with an introduction highlighting the importance of this prediction task in the medical domain. Traditional methods utilized for survival prediction are then explored, shedding light on their strengths and limitations. Following this, the paper delves into the application of traditional machine learning approaches for survival prediction. Subsequently, the focus shifts towards deep learning algorithms and their role in survival prediction. In conclusion, the paper identifies potential avenues for further research in this field, paving the way for continued advancements in cancer survival prediction.

Authors

Institutions

Publication Details

Journal
Transactions on Computer Science and Intelligent Systems Research
Published
2024-08-12
DOI
https://doi.org/10.62051/6bzv6x65
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Research on Machine Learning for Predicting Survival Time in Cancer Patients

Binghua Xi
Transactions on Computer Science and Intelligent Systems Research
Radiomics and Machine Learning in Medical Imaging
article

A Research on Machine Learning for Predicting Survival Time in Cancer Patients

Binghua Xi
article en

Abstract

Cancer remains one of the leading causes of mortality worldwide. Predicting the survival of cancer patients holds immense significance in guiding treatment decisions and enhancing patient quality of life. To provide a comprehensive understanding of the application of machine learning methods and to explore future research directions in survival prediction, this paper reviews the current landscape of machine learning algorithms in this domain. The paper discusses the significance and challenges associated with predicting the survival time of cancer patients. It begins with an introduction highlighting the importance of this prediction task in the medical domain. Traditional methods utilized for survival prediction are then explored, shedding light on their strengths and limitations. Following this, the paper delves into the application of traditional machine learning approaches for survival prediction. Subsequently, the focus shifts towards deep learning algorithms and their role in survival prediction. In conclusion, the paper identifies potential avenues for further research in this field, paving the way for continued advancements in cancer survival prediction.

Transactions on Computer Science and Intelligent Systems ResearchVol. 5
Shanghai Normal University (CN)
Good health and well-being
Openalex Percentile: Top 82%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Research on Machine Learning for Predicting Survival Time in Cancer Patients — Binghua Xi · Transactions on Computer Science and Intelligent Systems Research (2024) | TGRS Research Map | TGRS