Machine learning to reveal an astute risk predictive framework for Gynecologic Cancer and its impact on women psychology: Bangladeshi perspective

Background: In this research, an astute system has been developed by using machine learning and data mining approach to predict the risk level of cervical and ovarian cancer in association to stress. Results: For functioning factors and subfactors, several machine learning models like Logistics Regr...

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Bibliographic Details
Main Authors:	Ahmed, M.R (Author), Asaduzzaman, S. (Author), Bhuiyan, T. (Author), Chakraborty, S. (Author), Islam, M.S (Author), Rehana, H. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	Adaptive boosting algorithm Algorithms Barium compounds Bayes theorem Bayes Theorem Cervical cancers child Child Data mining Decision trees Diseases female Female Gynecologic cancer Gynecological cancer Health risks human Humans Logistic Models Logistic regression Logistics regressions machine learning Machine learning Machine Learning Machine learning models neoplasm Neoplasms Neural Networks, Computer Ovarian cancers Predictive analytics pregnancy Pregnancy Quadratic classifiers Significance levels Significant risk factors Smart prediction tool Standard metrics statistical model Women psychology
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Machine learning to reveal an astute risk predictive framework for Gynecologic Cancer and its impact on women psychology: Bangladeshi perspective

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