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Paper Details

A Machine Learning Approach to Predict SEER Cancer
Dm. Mehedi Hasan Abid, Dr Syed Akhter Hossain, Fahim Yusuf, Md. Assaduzzaman, Ms. Zahura Zaman, Professor Dr. Md. Ismail Jabiullah, Tariqul Islam,

The SEER database is among the persuading stores regarding malignancy pointers inside us. The SEER list helps impact investigation for the gigantic measure of patients’ bolstered viewpoints for the most part ordered as an insightful segment and impact. Assistant careful proof nearly the carcinoma dataset is ordinarily started on the site of the National Cancer Institute. The main point of this work is that depending on the individual’s manifestations, and we will foresee whether individuals are in danger of malignant growth or not. Perseverance and desire for the benefit of malignant growth patients have the option to upsurge prophetic exactitude and limit in the end cause better-educated decisions. To the current end, various amendments smear AI to disease data of the surveillance, epidemiology, and end results database. It may be used to better forecast cancer in the medical sector, and these studies can give a good chance to enhance existing models and build new models for uncommon cancers of minority groups in particular. In this paper, the authors contribute to getting more predicted accuracy for SEER cancer and use it to better forecast cancer in the medical sector.

"SEER cancer Carcinoma Epidemiology Machine learning Data mining"
Journal or Conference Name
Lecture Notes in Networks and Systems
Publication Year