EARLY RISK PREDICTION OF CERVICAL CANCER: A MACHINE LEARNING APPROACH

Authors

  • Syed Anas Ali, Mohammed Lateef Mahmood , Mohammed Abdul Zaheer B.E. Student, Department of IT, Lords Institute of Engineering and Technology, Hyderabad Author
  • Hajira Sabuhi Assistant Professor, Department of IT, Lords Institute of Engineering and Technology, Hyderabad Author

Abstract

Cervical cancer is a significant public health concern worldwide, necessitating early risk prediction
through Machine Learning (ML). This study employs eleven ML algorithms on a UCI ML repository dataset to
forecast risks. Initial results show Multi-Layer Perceptron (MLP) achieving 93.33% accuracy with default settings.
Further, hyperparameter tuning via Grid Search Cross Validation (GSCV) validates comparable performance
across K-Nearest Neighbours (KNN), Decision Tree Classifier (DTC), Support Vector Machine (SVM), Random
Forest Classifier (RFC), and MLP, all achieving 93.33% accuracy. This study underscores ML's potential in early
cervical cancer risk assessment, benefiting healthcare professionals and at-risk individuals through enhanced
predictive capabilities

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Published

2024-08-28

Issue

Vol. 14 No. 3 (2024): July-September

Section

Articles

How to Cite

EARLY RISK PREDICTION OF CERVICAL CANCER: A MACHINE LEARNING APPROACH. (2024). International Journal of Engineering and Science Research, 14(3), 485-495. https://www.ijesr.org/index.php/ijesr/article/view/944