• J Fathima Kaleema

Cervical cancer is the fourth most common cancer among women around the world. The objective of this study is to provide a comparative study to predict the cervical cancer dataset. The extraction involved over 38 attributes here used three different machine learning algorithms (XGBoost, Decision Tree, Logistic Regression) has been applied on four different medical tests (Biopsy, Cytology, Hinselmann, and Schiller) as four different target variables. The disease cannot be identified in the early stage. The result showed that the performance of EML outperforms other classifiers after evaluation. In this paper it exposes the classifiers can effectively achieve the best performance with the least number of highly important attributes.

Machine Learning, XGBoost , Decision Tree, Logistic Regression

"CERVICAL CANCER PREDICTION USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.a59-a61, August-2022, Available :http://www.jetir.org/papers/JETIR2208008.pdf

"CERVICAL CANCER PREDICTION USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. ppa59-a61, August-2022, Available at : http://www.jetir.org/papers/JETIR2208008.pdf