Predictive modeling and web-based tool for cervical cancer risk assessment : A comparative study of machine learning models
© 2024 The Authors. Published by Elsevier B.V..
In today's digital era, the rapid growth of databases presents significant challenges in data management. In order to address this, we have developed and designed CHAMP (Cervical Health Assessment using machine learning for Prediction), which is a user interface tool that can effectively and efficiently handle cervical cancer databases to detect patterns for future prediction diagnosis. CHAMP employs various machine learning algorithms which include XGBoost, SVM, Naive Bayes, AdaBoost, Decision Tree, and K-Nearest Neighbors in order to predict cervical cancer accurately. Moreover, this tool also designates to evaluate and optimize processes, to retrieve the significantly augmented algorithm for predicting cervical cancer. Although, the developed user interface tool was implemented in Python 3.9.0 using Flask, which provides a personalized and intuitive platform for pattern detection. The current study approach contributes to the accurate prediction and early detection of cervical cancer by leveraging the power of machine learning algorithms and comprehensive validation tools, which aim to provide learned decision-making.•CHAMP is a user interface tool which is designed for the detection of patterns for future diagnosis and prognosis of cervical cancer.•Various machine learning algorithms are employed for accurate prediction.•This tool provides personalized and intuitive data analysis which enables informed decision-making in healthcare.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
MethodsX - 12(2024) vom: 01. März, Seite 102653 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chauhan, Ritu [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 26.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.mex.2024.102653 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © 2024 The Authors. Published by Elsevier B.V. | ||
| 520 | |a In today's digital era, the rapid growth of databases presents significant challenges in data management. In order to address this, we have developed and designed CHAMP (Cervical Health Assessment using machine learning for Prediction), which is a user interface tool that can effectively and efficiently handle cervical cancer databases to detect patterns for future prediction diagnosis. CHAMP employs various machine learning algorithms which include XGBoost, SVM, Naive Bayes, AdaBoost, Decision Tree, and K-Nearest Neighbors in order to predict cervical cancer accurately. Moreover, this tool also designates to evaluate and optimize processes, to retrieve the significantly augmented algorithm for predicting cervical cancer. Although, the developed user interface tool was implemented in Python 3.9.0 using Flask, which provides a personalized and intuitive platform for pattern detection. The current study approach contributes to the accurate prediction and early detection of cervical cancer by leveraging the power of machine learning algorithms and comprehensive validation tools, which aim to provide learned decision-making.•CHAMP is a user interface tool which is designed for the detection of patterns for future diagnosis and prognosis of cervical cancer.•Various machine learning algorithms are employed for accurate prediction.•This tool provides personalized and intuitive data analysis which enables informed decision-making in healthcare | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Cervical cancer | |
| 650 | 4 | |a Early detection | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Predictive Modeling and Web-Based Tool for Cervical Cancer Risk Assessment: A Comparative Study of Machine Learning | |
| 650 | 4 | |a Predictive modeling | |
| 650 | 4 | |a Risk assessment | |
| 650 | 4 | |a Web-based tool | |
| 650 | 4 | |a XGBoost | |
| 700 | 1 | |a Goel, Anika |e verfasserin |4 aut | |
| 700 | 1 | |a Alankar, Bhavya |e verfasserin |4 aut | |
| 700 | 1 | |a Kaur, Harleen |e verfasserin |4 aut | |
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