• Nalin Pawar
• Akhilesh Kumar Bhardwaj

Artificial intelligence has benefited in medical research advancement. Specialists have been enlivened to configuration programs that help malignant growth location and guess because of the accessibility of open-source medical services insights. In these conditions, profound learning and AI models give a dependable, quick, and viable strategy for managing such troublesome illnesses. In 2015, malignant growth was the second greatest reason for mortality around the world, representing 8.8 million fatalities. It's been depicted as a "heterogeneous infection" with various assorted subtypes. Early identification and forecast of a disease type has turned into a need in malignant growth research since it can assist with patient clinical treatment. It is critical to identify between benign and malignant tumours accurately in order to make better treatment judgments. Despite the intricate interconnections of high-dimensional medical data, statistical methods have traditionally been employed to classify high-risk and low-risk cancer. AI has created as a suitable philosophy for overseeing high-layered information, with expanding appropriateness in clinical choice help, to conquer the burdens of customary factual strategies. This study examines the primary issues associated with machine learning algorithms in cancer diagnosis as well as future research opportunities. Keywords: Cancer, Machine learning, Classification, Prediction and Diagnosis.

"A Broad Review: Causes, Challenges and Various Machine Learning Techniques for Detection of Cancers ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.e267-e271, August-2022, Available :http://www.jetir.org/papers/JETIR2208431.pdf

"A Broad Review: Causes, Challenges and Various Machine Learning Techniques for Detection of Cancers ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. ppe267-e271, August-2022, Available at : http://www.jetir.org/papers/JETIR2208431.pdf