Detection Of Breast Cancer By Machine Learning Algorithms

According to an NHS survey, 10 women among 80 are diagnosed with breast cancer in their lives. Nowadays, breast cancer is becoming more common and is detected in women between the ages of 29 and 50. In a very rare case, even men can be tested and diagnosed with breast cancer. Multiple tests and procedures are frequently used in traditional cancer diagnosis systems, which puts patients under physical and emotional strain. Machine learning (ML) algorithms have become potential methods for precise and effective breast cancer diagnosis in an effort to lessen this burden. In the initial stage, with very few symptoms, it can be detected through mammographic screening, and from this, it can be classified as benign or malignant. This can increase the rate of survival. Most of the cancer detection system, which takes a lot of time for detection with physical and mental stress, will involve many tests that can cause mental and physical breakdowns by considering all the aspects. The detection of cancerous and noncancerous cells can be done using machine learning (ML) algorithms like logistic regression, random forest, and K-nearest neighbour (KNN) algorithms. These ML algorithms may be used to analyse mammographic data to measure the efficacy and accuracy of breast cancer diagnosis. By examining the patterns and traits seen in mammograms, these algorithms can help identify possible anomalies suggestive of breast cancer. Healthcare workers may speed up the detection process and cut down on the time and stress involved with conventional procedures by utilising the power of ML.By Applying these algorithms to the mammogram data, we can detect the accuracy and efficiency of breast cancer detection.