Analyzing the Accuracy of AI Techniques for Breast Cancer Detection
Sai Lasya Naraparaju, Naga Sai Nikhitha Eerlapalli, Nikitha Modem, Reshmi Paidipati, Chinnasamy Karthikeyan, K Sathish Kumar · 2023
Breast cancer, the foremost prevailing style of cancer in girls, accounts for 14 percent of cancer cases in Indian girls. In keeping with reports, a girl in India receives a carcinoma designation every four minutes. Each rural and concrete Republic of India area unit seeing a rise in carcinoma cases. Girls in their early thirties to fifties area unit at a high risk of developing carcinoma as a result of it’s the foremost prevailing style of cancer in Indian women; this risk rises till it reaches its peak by the time they’re fifty to sixty-four years previous. Most breast cancers are carcinomas, which are tumors that start in the epithelial cells that line organs and tissues throughout the body. In India, the period risk of developing carcinoma is one in twenty-eight girls. The unfolding of the malady may be stopped by making machine-based carcinoma forecasts, which can speed up the time it takes to find carcinoma. Machine learning uses a spread of applied mathematics, probabilistic, and optimization techniques to change the system to find out from previous experiences and notice difficult-to-detect patterns from strident or advanced information sets. This work provides you with a fast summary of machine learning strategies combined with cybersecurity that aid in women’s carcinoma early detection. Finding the foremost correct formula to predict carcinoma in terms of its categories is the main goal of this work. This study assessed the effectiveness and potency of every algorithm’s information classification. conjointly contrasted with alternative works that are printed in this field. The algorithms that have been used to train the model are Logistic Regression, Random Forest, Decision Tress. Among the three models, random forest has the highest accuracy while logistic regression has the highest precision. The main purpose of the research is to find the best algorithm for breast cancer detection. Our primary goal is to find the best-suited algorithm with high accuracy and good F1 score, precision, and recall.