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Welcome to the Breast-Cancer wiki! A Comparative Analysis of Breast Cancer ML/AI Binary Classifications
This study is dedicated to #BreastCancerAwarenessMonth2022 #breastcancer #BreastCancerDay @Breastcancerorg @BCAction @BCRFcure @NBCF @LivingBeyondBC @breastcancer @TheBreastCancer @thepinkribbon @BreastCancerNow.
One of the most common cancer types is breast cancer (BC), and early diagnosis is the most important thing in its treatment. Recent studies have shown that BC can be accurately predicted and diagnosed using machine learning (ML) technology.
Specifically, ML allows the integration or combination of different layers of data, such as those from medical images, laboratory results, clinical outcomes, biomarkers, and biological features for better prognostication and stratification of BC patients.
Our objective is to compare different supervised ML, deep learning (DL) and data mining techniques for the early detection of BC. The idea is to analyze BC data based on its characteristics and identify the effectiveness of clustering and classification instructions for analyzing and fitting various ML models. We tested the performance of ML models by looking at their accuracies, sensitivities, specificities, and other metrics. Results obtained with the best ML model with most dominant features included showed the highest classification accuracy (~99%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the BC research.
State-of-the-Art Scope Methodology Prerequisites Scikit-Learn Dataset Feature Boxplots vs X-Plots EDA Error Bar QC Binary PCA Clusters %matplotlib notebook #for loop ends #for loop ends plt.show() LR vs KNN Decision Boundaries FE + HPO + LR + GB Models EDA + HPO + 5 Models TechVidvan NN Model 8 Input Images + LR + SVM + NN Models CoderzColumn ML Interpretation Lime LR Model Explanation Conclusions Continue Reading References
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