• Khadeeja Naqvi
• Divyanshi Gautam
• Ashish Kumar Srivastava
• Prof. (Dr.) Syed Qamar Abbas
• Dr. Nikhat Akhtar

One of the most hazardous illnesses for people is cancer, yet there is currently no long-term treatment available. The most frequent cause of cancer-related mortality is breast cancer. Finding cancer in its early stages is crucial. While rates are rising in practically every location worldwide, they are greater among women in more developed areas. But while it's still in its early stages, the cancer can still be cured. The prognosis and recovery of breast cancer patients are enhanced by early identification and rapid, efficient therapy. When identifying tumors, there is a significant chance of ambiguity and inaccurate detection, which has to be addressed. If patients are appropriately categorized, unnecessary treatments can be avoided. Medical imaging research now heavily relies on machine learning (ML). Data classification techniques based on machine learning are efficient. Particularly in the realm of medicine, where those techniques are frequently utilized in diagnosis and analysis for decision-making. Based on the features supplied by the data, we employ a variety of machine learning techniques to predict whether a tumor is benign or malignant in this situation. In this article, we are presented a system that can identify breast cancer and discussed how machine learning (ML) algorithms might enhance breast cancer early detection and diagnosis.

Machine Learning, Breast Cancer, Data Exploration, Classification, Convolutional Neural Network (CNN), UCI Machine Learning Repository.

"A Machine Learning-Based Rational Breast Cancer Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.g558-g567, July-2022, Available :http://www.jetir.org/papers/JETIR2207677.pdf

"A Machine Learning-Based Rational Breast Cancer Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppg558-g567, July-2022, Available at : http://www.jetir.org/papers/JETIR2207677.pdf