Abstract
One of the diseases, which causes more deaths frequently and in more numbers, is Breast Cancer. Let us go through the index for global statistics for breast cancer (BC), which impacts women globally and it causes a lot of trouble to the health of women in turn causing deaths. So it is a threat to society for causing a lot of trouble and the majority of the new cases are suffering from breast cancer. A tumor, which is causing deaths of women around the globe, is known by the name malignant. As is consensus, early diagnosis of any medical disorders, such as cancer or malignant diseases, will always increase the likelihood of survival since patients can receive early or preemptive clinical treatment. It will be helpful in avoiding unnecessary therapies if benign tumors are classified more precisely. So, it is very imperative to precisely diagnose the malignant status, whether it is benign or malignant, for the detection of breast cancer, which has become an extremely rapidly developing field of study. On a complex dataset, if we want to predict or forecast BC patterns, machine learning models will be very useful as they can classify different patterns more accurately than any other general algorithms. Artificial Intelligence models are useful for properly grouping datasets, particularly in the healthcare arena, where these models are frequently used to reach conclusions and are helpful in predicting. While predicting using Logistic Regression, the exactness was calculated; later the same is compared with Decision Tree and Random Forest Classifier to give the best method for predicting breast cancer on a dataset available to us. The main goal is to evaluate each model’s accuracy and precision in terms of productivity and exactness for accuracy, precision, f1 score, and support. According to the findings, the Random Forest Classifier has the highest precision (96.50%) when assessing the data, followed by Decision Tree (93.70%), and Logistic Regression (95.10%). All of the trials are carried out using AI tools in a reenactment.
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Makka, S., Arora, G., Reddy, S., Lingam, S. (2023). Use of Machine Learning Models for Analyzing the Accuracy of Predicting the Cancerous Diseases. In: Bhattacharya, A., Dutta, S., Dutta, P., Piuri, V. (eds) Innovations in Data Analytics. ICIDA 2022. Advances in Intelligent Systems and Computing, vol 1442. Springer, Singapore. https://doi.org/10.1007/978-981-99-0550-8_13
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