Breast Cancer Classification using Python Programming in Machine Learning

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IJERTV9IS080359
Breast Cancer Classification using Python Programming in Machine Learning

Shruthi S , Binu Xavier F , Ravi Kumar A , Yeshwanth S, Dr. Mahalinga V Mandi

Breast cancer is a disease in which cells in the breast grow out of control in a rapidly. Breast cancer occurs when a malignant (cancerous) tumor originates in the breast cells. It is the most commonly occurring cancer in women and the second most common cancer overall. Around 2 million cases were observed in 2018. The early diagnosis of breast cancer can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients affected. Further accurate classification from the data of benign tumors can prevent patients from undergoing unnecessary treatments. Thus, the correct diagnosis of breast cancer and the classification of patients into malignant or benign groups is the subject of all research done and observed. Because of its unique advantages in critical features detection from complex breast cancer datasets, machine learning (ML) is widely recognized as the methodology of choice in Breast Cancer pattern classification. This project is a relative study of the implementation of models using Logistic Regression, SVM, KNN, Random Forest, and Decision tree, which is done on the data set taken from the UCI repository. We have obtained the highest accuracy for the random forest that is 97%. We have also obtained the accuracy of 95%, 93%, 95%, 94% for logistic regression, SVM, KNN and Decision tree respectively