Modeling a Logistic Regression based Sustained Approach for Cancer Detection

This assessment and treatment of cancer may be done using logistic regression. To properly forecast whether a tumour is malignant or benign, the likelihood of binary outcomes may be simulated based on input variables and taken into account for factors like volume, topology and texture. It aids in ri...

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Bibliographic Details
Published in2023 International Conference for Technological Engineering and its Applications in Sustainable Development (ICTEASD) pp. 262 - 267
Main Authors Pattanaik, Ashis, Gour, Preety, Mishra, Sushruta, Sharma, Vandana, Alabdeli, Haider
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.11.2023
Subjects
Accuracy
Binary outcome
Cancer detection
Cancer diagnosis
Logistic regression
Predictive models
Risk management
Survival analysis
Sustainable development
Topology
Tumour classification
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Summary:This assessment and treatment of cancer may be done using logistic regression. To properly forecast whether a tumour is malignant or benign, the likelihood of binary outcomes may be simulated based on input variables and taken into account for factors like volume, topology and texture. It aids in risk assessment by estimating an individual's likelihood of developing cancer using factors like age-group, relatives past data, life choices and gene based markers. Logistic regression plays an important role in early cancer detection and creating screening tools that identify high-risk individuals through patent characteristics, biomarkers, and medical imaging data. Prediction of the probability of survival based on age, tumor characteristics, treatment options and comorbidities is useful for survival analysis. In a comparative study, logistic regression achieved a high accuracy of 97.4%, along with random forest, in cancer detection and diagnosis.
DOI:10.1109/ICTEASD57136.2023.10585150