Machine Learning Approaches for Early Detection of Cervical Cancer
Inam Ul Haq, Janvi Malhotra, Vanshika Rawat, Jyoti Kumari, Gagandeep Kaur · 2025
Cervical cancer remains as one of the main challenges to global health, particularly in low- or middle-income countries since few women are aware of the screening services and vaccines. Screening for cervical cancer should be conducted at the furthest levels possible to increase the chances of patients responding positively to treatment, decrease mortality rates, and enhance the quality of life. This paper aims at examining the use of machine learning algorithms in the prediction of cervical cancer based on data from the health department of the UCI repository. Due to its applied nature, the preprocessing of the dataset occurred to eliminate all forms of missing and noisy data as well as normalization of all feature sets to make the model more reliable and accurate. A comparative analysis was performed by training multiple classifiers on our data; these were XGBoost classifier, random forest classifier, extra trees classifier, bagging classifier, and decision tree classifier. Accuracy was adopted as the main benchmark for evaluating which of the models performed best. The dataset factored four target variables that define many other diagnostic tests and indices such as Hanselman and Schiller, cytology, and biopsy. These findings revealed that the Hanselman and Schiller model provided the best performing at 97.01%, while biopsy and cytology at 96.51% and 95.52%, respectively. Such results imply the effectiveness of the machine learning methodologies for improving the decision-risk models used for diagnosis of cervical cancer. The study also gives emphasis on the possibility of examining the multivariable datasets, which can be used in detecting signs of cervical cancer in the early stage. These predictive models can potentially help the healthcare practitioners incorporate early diagnostic and preventive actions where resources are likely to be scarce. This research forms part of the literature in the use of artificial intelligence in healthcare especially focusing on the application of machine learning in managing pressing issues in cancer diagnosis. Possible subsequent studies might include the use of other databases, the assessment of the HFFS in different populations, and investigation into the techniques for making the results interpretable for practical and ethical applications in clinical practice. The paper proves the positive impact of data analysis solutions on developing precision medicine and enhancing the quality of population's health.