• sushma T N
• susheela N

In almost all countries, precautionary measures are less expensive than medical treatment. The early prediction of any disease gives a patient better chances of successful treatment than disease discovery at an advanced stage of its development. If we do not know how to treat patients, any treatment we can provide would be useful and would provide a more comfortable life. Cervical cancer is one such disease, considered to be fourth among the most common types of cancer in women around the world. There are many factors that increase the risk of cervical cancer, such as age and use of hormonal contraceptives. Early detection of cervical cancer helps to raise recovery rates and reduce death rates. In our proposed work, we used Machine Learning algorithms to find a model capable of diagnosing cervical cancer with high accuracy and sensitivity. The cervical cancer risk factor dataset was used to construct the classification model through a voting method that combines three classifiers: Decision Tree, K-N Neighbor and Random Forest.

KeyWords: Machine Learning(ML), Decision Tree(DT), Random Forest(RF), K-Nearest Neighbor(KNN).

""Predictive Modeling for Early Cervical Cancer Diagnosis Using ML Algorithms"", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.e491-e496, February-2025, Available :http://www.jetir.org/papers/JETIR2502457.pdf

""Predictive Modeling for Early Cervical Cancer Diagnosis Using ML Algorithms"", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppe491-e496, February-2025, Available at : http://www.jetir.org/papers/JETIR2502457.pdf