• Reuel Philip
• Aanaditha
• Gnana Teja
• Dr. N Ch. Sriman Narayana Iyengar

Cancer is mostly recognized in humans, the reason for the mounting rate. The discovery of disease detection takes a lengthy time physically and little convenience of the system, an obligation to alter automatic diagnosis classification primary identification of cancer. For cancer tumor finding, castoff classification methods of machine learning and ensemble machine learning for earliest data can guess the kind of novel input data. In this paper emphasis on employment of models is complete on the dataset. The outcome of accuracy, precision, recall and False Positive Rate The efficiency of ensemble machine learning algorithms is measured and compared with other algorithms' results. From statistical analysis, the ensemble model predicts the accuracy rate is 89.3 % for decision tree and random forest models. 90.4% for logistic regression and support vector models. If you compare these two ensemble models with individual models provide a higher accuracy rate.

Ensemble Machine Learning, Cancer, Prediction, Accuracy.

"ENSEMBLE MACHINE LEARNING TECHNIQUES FOR CANCER PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.k647-k654, April-2024, Available :http://www.jetir.org/papers/JETIR2404A88.pdf

"ENSEMBLE MACHINE LEARNING TECHNIQUES FOR CANCER PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppk647-k654, April-2024, Available at : http://www.jetir.org/papers/JETIR2404A88.pdf