A Distincitve Model to Classify Tumor Using Random Forest Classifier

The distinctive machine learning model that was created as a need for doctors/ oncologists who treat patients in critical stages. The present day has many people suffering from cancer where they get diagnosed only during the last stage (4^th stage) of cancer. This leads to many untimely deaths of their loved ones for many people. To reduce such risks and provide more effort in saving those lives, this model may be used. This model is made from Random Forest classlfier^[1] where it classifies a tumor to be either Benign(Non-cancerous) or Malignant(Cancerous). It uses 10 features of tumor subdivided into mean, standard error and worst case value of each to increase its accuracy. The inputs given to this model are obtained from medical imaging and hence do not need any medical tests where time may be wasted. The future of this model relies on the demand where it may lie in being developed into an application or it may be developed into a full-fledged health-care system. The main objective of this model, is to ensure that more time can be bought to save or extend the lifetime of the patient by providing chemotherapy as a preventive measure for an untimely death that may occur. This model predicts with 94.34% accuracy, 93% best case confidence and 56% worst case confidence whether the given data resembles a malignant or benign tumor.