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Cancer is now a day's one of the main diseases which has widely affected among the peoples. A molecular pathologist selects a list of genetic variations of interest that he/she wants to analyze. The molecular pathologist searches for evidence in the medical literature that somehow is relevant to the genetic variations of interest Finally this molecular pathologist spends a huge amount of time detecting the evidence which is related to each of the variations to classify them. The ultimate goal is to replace step 3 by a machine learning model. The molecular pathologist will still have to decide which variations area of interest, and also collect the relevant evidence. In this paper, we apply machine learning methods especially logistic regression (which is more accurate) on the datasets to determine and examine whether there are any signs or possibilities of cancer and if the person is examined as cancerous then the stage of cancer is also determined. Cancer disease is classified into four types named type 1, type 2, type 3 and type 4. Id, Gene, variation, and class are the fields used.

Detection Analysis of Various Types of Cancer by Logistic Regression using Machine Learning