Prediction of patient breast cancer probability

Abstract

Breast cancer has a significant global impact; in 2015, it caused 570,000 deaths and 1.5 million yearly diagnoses. A challenge is that it has a poor prognosis for cure and is metastatic. The 21st century's health-conscious atmosphere emphasizes the need to lower the death toll from cancer, which will account for approximately one in six fatalities in 2020. According to malignancy, an estimated 7.8 million women are projected to be diagnosed with breast cancer throughout the upcoming five-year period. For the identification and prevention of cancer, proactive measures are required. Python algorithms, particularly linear regression, are extraordinarily useful for analyzing complex datasets. Using linear regression in Python to analyze data yields illuminating models that reveal morbidity trends. With the insights gained from these models, healthcare providers can provide patients with more individualized care. This proactive approach and implementing Python's linear regression algorithms enhance the understanding of cancer risk and allow for effective preventative measures. Greater public awareness of health issues has resulted in an emphasis on preventative measures against breast cancer and other cancers. With the help of Python's data-driven algorithms, society may acquire a more accurate understanding of cancer risks and make decisions that will enhance patient welfare.