Machine Learning for Breast Cancer Detection
Machine learning is a science that powers our computers and acts more efficiently without being additionally programmed differently. In fact, since decades, machine learning has boomed us with the self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human needs. Machine learning is so demanding these days that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.
Machine Learning in the Medical Field: -
One of the major reasons these days for female death in the world is breast cancer. Very commonly used to distinguish malignant tumors from benign ones is mammography. In this mammographic diagnostic 5 machine learning predictions are used to detect breast cancer biopsy such as :1. Logistic Regression (LR) 2. Linear Discriminant Analysis (LDA) 3. Quadratic Discriminant Analysis (QDA) 4. Random Forest (RF) 5. Support Vector Machine (SVM) classification The final diagnosis results notify that SVM (support vector machine) classification performs better than other with an accuracy of 95.8% in diagnosing both malignant and benign breast cancer, a sensitivity of 98.4% in diagnosing disease, a specificity of 94.4%. Moving ahead the ROC (receiver operating characteristic) curve for SVM (support vector machine) was 99.9% for predictions, diagnosis and outcome accuracy for Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forest (RF) methods. Hence, the SVM (support vector machine) reports or predictions for malignant cases for breast cancer is highly accurate and consistent.
To analyze medical data, various data mining and machine learning methods are available. An important challenge in data mining and machine learning areas is to build accurate and computationally efficient classifiers for Medical applications. It shows that
the SVM has proven its efficiency in Breast Cancer prediction and diagnosis and achieves the best performance in terms of precision and low error rate.
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