Predictive modeling and web-based tool for cervical cancer risk assessment: A comparative study of machine learning...

Area of Science:

• Medical Informatics
• Machine Learning in Healthcare
• Oncology Data Analysis

Background:

• The exponential growth of digital data presents challenges in managing large medical databases.
• Effective data management is crucial for accurate disease prediction and diagnosis.
• Cervical cancer diagnosis relies on timely pattern detection within complex datasets.

Purpose of the Study:

• To develop CHAMP (Cervical Health Assessment using machine learning for Prediction), a user interface tool for cervical cancer data analysis.
• To leverage machine learning algorithms for accurate prediction and early detection of cervical cancer.
• To provide an intuitive platform for pattern detection and informed clinical decision-making.

Main Methods:

• Implementation of CHAMP using Python 3.9.0 and Flask framework.
• Integration of multiple machine learning algorithms: XGBoost, SVM, Naive Bayes, AdaBoost, Decision Tree, and K-Nearest Neighbors.
• Evaluation and optimization of algorithms for enhanced predictive accuracy in cervical cancer detection.

Main Results:

• CHAMP effectively handles cervical cancer databases for pattern detection and prediction.
• The tool employs various machine learning algorithms to achieve accurate cervical cancer prediction.
• Personalized and intuitive data analysis facilitates informed decision-making.

Conclusions:

• CHAMP offers a robust solution for managing cervical cancer data and improving diagnostic accuracy.
• The application of machine learning algorithms enhances the potential for early cervical cancer detection.
• This tool empowers healthcare providers with data-driven insights for improved patient prognosis.