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10.1016/j.amsu.2020.12.043 http://scihub22266oqcxt.onion/10.1016/j.amsu.2020.12.043 33489117!7806524!33489117     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Ann+Med+Surg+(Lond) 2021 ; 62 (ä): 53-64 Nephropedia Template TP {{tp|p=33489117|t=2021. Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer |pdf=|usr=}}{{33489117}} gab.com Text Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer JO: Ann Med Surg (Lond) http://www.kidney.de/pget.php?&tw=1&pmid=33489117 #FOAvip BACKGROUND: Breast cancer disease is the most common cancer in US women and the second cause of cancer death among women. OBJECTIVES: To compare and evaluate the performance and accuracy of the key supervised and semi-supervised machine learning algorithms for breast cancer prediction. MATERIALS AND METHODS: We have used nine machine learning classification algorithms for supervised (SL) and semi-supervised learning (SSL): 1) Logistic regression; 2) Gaussian Naive Bayes; 3) Linear Support vector machine; 4) RBF Support vector machine; 5) Decision Tree; 6) Random Forest; 7) Xgboost; 8) Gradient Boosting; 9) KNN. The Wisconsin Diagnosis Cancer dataset was used to train and test these models. To ensure the robustness of the model, we have applied K-fold cross-validation and optimized hyperparameters. We have evaluated and compared the models using accuracy, precision, recall, F1-score, and ROC curves. RESULTS: The results of all models are inspiring using both SL and SSL. The SSL has high accuracy (90%-98%) with just half of the training data. The KNN model for the SL and logistic regression for the SSL achieved the highest accuracy of 98. CONCLUSION: The accuracies of SSL algorithms are very close to the SL algorithms. The accuracies of all models are in the range of 91-98%. SSL is a promising and competitive approach to solve the problem. Using a small sample of labeled and low computational power, the SSL is fully capable of replacing SL algorithms in diagnosing tumor type. Twit Text FOAVip Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer ????Ann Med Surg (Lond) http://www.kidney.de/pget.php?&tw=1&pmid=33489117 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33489117 #FOAvip English Wikipedia <ref>{{Cite pmid|33489117}}</ref> Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer #MMPMID33489117 Al-Azzam N; Shatnawi I Ann Med Surg (Lond) 2021[Feb]; 62 (ä): 53-64 PMID33489117show ga BACKGROUND: Breast cancer disease is the most common cancer in US women and the second cause of cancer death among women. OBJECTIVES: To compare and evaluate the performance and accuracy of the key supervised and semi-supervised machine learning algorithms for breast cancer prediction. MATERIALS AND METHODS: We have used nine machine learning classification algorithms for supervised (SL) and semi-supervised learning (SSL): 1) Logistic regression; 2) Gaussian Naive Bayes; 3) Linear Support vector machine; 4) RBF Support vector machine; 5) Decision Tree; 6) Random Forest; 7) Xgboost; 8) Gradient Boosting; 9) KNN. The Wisconsin Diagnosis Cancer dataset was used to train and test these models. To ensure the robustness of the model, we have applied K-fold cross-validation and optimized hyperparameters. We have evaluated and compared the models using accuracy, precision, recall, F1-score, and ROC curves. RESULTS: The results of all models are inspiring using both SL and SSL. The SSL has high accuracy (90%-98%) with just half of the training data. The KNN model for the SL and logistic regression for the SSL achieved the highest accuracy of 98. CONCLUSION: The accuracies of SSL algorithms are very close to the SL algorithms. The accuracies of all models are in the range of 91-98%. SSL is a promising and competitive approach to solve the problem. Using a small sample of labeled and low computational power, the SSL is fully capable of replacing SL algorithms in diagnosing tumor type. äComparing supervised and semi-supervised Machine Learning Models on Di(epmc).pdf DeepDyve Pubget Overpricing