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Predict Breast Cancer using Deep Learning

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Ayesha Shafique

Predict Breast Cancer using Deep Learning

Engineering
1 of 19
Pakistan has highest incidence of breast cancer in Asia. Artificial Intelligence in Healthcare
Each year more than 83,000 cases of breast cancer are reported in Pakistan. Artificial Intelligence in Healthcare
Mortality due to cancer in females of all ages in Pakistan. Artificial Intelligence in Healthcare 40% 7% 6% 4% 3% 3% 3% 3% 2% 29% Breast Lip, oral cavity Cervix Uteri Ovary Oesophagus Non-Hodgkin lymphoma Colorectum Corpus uteri Leukarmia Other ans unspecified
Hope is the physician of each misery.. Artificial Intelligence in Healthcare
Early Detection=Better Outcomes If a breast cancer is detected early, there is 100% 5-year relative survival rate. Artificial Intelligence in Healthcare
Statistics for the overall survival rates for breast cancer based on certain stages of disease development. Artificial Intelligence in Healthcare
How to detect a breast cancer at the early stage because there is no system of mass diagnosis in our country? Artificial Intelligence in Healthcare
Let Artificial Intelligence to assist oncologist in diagnosing this ‘Sad’ disease. Artificial Intelligence in Healthcare
Artificial Intelligence in Healthcare Data Source Data Pre- processing Methodology Results Conclusion
Breast Cancer data of 683 women was obtained from the UC Irvine Machine Learning Repository. URL to access data: https://archive.ics.uci.edu/ml/datasets/br east+cancer+wisconsin+(original) Artificial Intelligence in Healthcare
Malignant Tumor: Cancerous Benign Tumor: Not-Cancerous Artificial Intelligence in Healthcare
Artificial Intelligence in Healthcare Missing value handling Outliers detection Clean data
Eight different AI/ML Models are studied on this dataset. Artificial Intelligence in Healthcare Multi-Layer Perceptron (MLP) Support Vector Machine (SVM) with Radial Basis Function kernel XGBoost Classifier Support Vector Machine (SVM) with linear kernel AdaBoost Classifier K-Nearest Neighbors (KNN) Logistic Regression Random Forest Classifier
Let the ML models train and show their magic! Artificial Intelligence in Healthcare
On unseen data, MLPClassifier model had the highest precision and sensitivity to predict malignant tumors (>96%). So our model is 96% accurate on real data !! Artificial Intelligence in Healthcare
Results on Breast Cancer Testing Dataset 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 Benign Malignant Benign Malignant precision sensitivity Breast Cancer Study MLP XGBoost Classifier Random Forest Logistic Regression SVM Linear Kernel SVM RBF Kernel KNN AdaBoost Artificial Intelligence in Healthcare
This machine-learning model can help breast cancer radiologist to assist them in predicting the malignant tumors at the early stage. Early stage detection not only leads to less treatment but also improves the chances of survival from breast cancer. Artificial Intelligence in Healthcare
Feel free to access the code: https://github.com/AyeshaShafique/B reast-Cancer-Study.git Artificial Intelligence in Healthcare

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Predict Breast Cancer using Deep Learning

  • 1.
  • 2. Pakistan has highest incidence of breast cancer in Asia. Artificial Intelligence in Healthcare
  • 3. Each year more than 83,000 cases of breast cancer are reported in Pakistan. Artificial Intelligence in Healthcare
  • 4. Mortality due to cancer in females of all ages in Pakistan. Artificial Intelligence in Healthcare 40% 7% 6% 4% 3% 3% 3% 3% 2% 29% Breast Lip, oral cavity Cervix Uteri Ovary Oesophagus Non-Hodgkin lymphoma Colorectum Corpus uteri Leukarmia Other ans unspecified
  • 5. Hope is the physician of each misery.. Artificial Intelligence in Healthcare
  • 6. Early Detection=Better Outcomes If a breast cancer is detected early, there is 100% 5-year relative survival rate. Artificial Intelligence in Healthcare
  • 7. Statistics for the overall survival rates for breast cancer based on certain stages of disease development. Artificial Intelligence in Healthcare
  • 8. How to detect a breast cancer at the early stage because there is no system of mass diagnosis in our country? Artificial Intelligence in Healthcare
  • 9. Let Artificial Intelligence to assist oncologist in diagnosing this ‘Sad’ disease. Artificial Intelligence in Healthcare
  • 10. Artificial Intelligence in Healthcare Data Source Data Pre- processing Methodology Results Conclusion
  • 11. Breast Cancer data of 683 women was obtained from the UC Irvine Machine Learning Repository. URL to access data: https://archive.ics.uci.edu/ml/datasets/br east+cancer+wisconsin+(original) Artificial Intelligence in Healthcare
  • 13. Artificial Intelligence in Healthcare Missing value handling Outliers detection Clean data
  • 14. Eight different AI/ML Models are studied on this dataset. Artificial Intelligence in Healthcare Multi-Layer Perceptron (MLP) Support Vector Machine (SVM) with Radial Basis Function kernel XGBoost Classifier Support Vector Machine (SVM) with linear kernel AdaBoost Classifier K-Nearest Neighbors (KNN) Logistic Regression Random Forest Classifier
  • 15. Let the ML models train and show their magic! Artificial Intelligence in Healthcare
  • 16. On unseen data, MLPClassifier model had the highest precision and sensitivity to predict malignant tumors (>96%). So our model is 96% accurate on real data !! Artificial Intelligence in Healthcare
  • 17. Results on Breast Cancer Testing Dataset 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 Benign Malignant Benign Malignant precision sensitivity Breast Cancer Study MLP XGBoost Classifier Random Forest Logistic Regression SVM Linear Kernel SVM RBF Kernel KNN AdaBoost Artificial Intelligence in Healthcare
  • 18. This machine-learning model can help breast cancer radiologist to assist them in predicting the malignant tumors at the early stage. Early stage detection not only leads to less treatment but also improves the chances of survival from breast cancer. Artificial Intelligence in Healthcare
  • 19. Feel free to access the code: https://github.com/AyeshaShafique/B reast-Cancer-Study.git Artificial Intelligence in Healthcare

Editor's Notes

  1. Relevant Papers: Wolberg, W.H., & Mangasarian, O.L. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In Proceedings of the National Academy of Sciences, 87, 9193--9196. [Web Link] Zhang, J. (1992). Selecting typical instances in instance-based learning. In Proceedings of the Ninth International Machine Learning Conference (pp. 470--479). Aberdeen, Scotland: Morgan Kaufmann. [Web Link]