Implementation of Data Mining in Breast Cancer Diagnosis Classification Using Logistic Regression Algorithm
Implementasi Data Mining dalam Klasifikasi Diagnosa Kanker Payudara Menggunakan Algoritma Logistic Regression
DOI:
https://doi.org/10.21070/ups.2767Keywords:
Logistic Regression, Breast Cancer Diagnosis Clasification, Confusion MatrixAbstract
Breast cancer is a very dangerous disease. It is considered as one of the most serious threats to women's health. To treat breast cancer, surgery and chemotherapy are two common approaches. It is important to diagnose breast cancer early to minimize the severity and increase the chance of cure. This study aims to classify breast cancer diagnoses using Logistic Regression. The data used is secondary data downloaded from Kaggle.com totaling 569 records. After going through pre-processing, the data that is ready to be processed is then divided into training and testing data with a ratio of 70%: 30%. This study resulted in an accuracy rate of 98% for predicting breast cancer patients after classification modeling and model testing using the confusion matrix method.
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