anomaly detection using single class svm with gaussian kernel

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Method to Improve Breast Cancer Diagnosis Anoop Vasant Kumar

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Page 1: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

Method to Improve Breast Cancer

Diagnosis

Anoop Vasant Kumar

Page 2: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

ProblemPreventable medical error is a big

killer.In the US alone, 400,000 people die

every year due to avoidable medical error in hospitals - this is equivalent to

TWO JUMBO JETS crashing every single day!

--NHS sets aside 26.1 Billion Dollars

to settle outstanding negligences and liabilities in clinical safety.

Page 3: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

Causes of Avoidable Medical Errors

Procedures and training methods not reformed, so mistakes happen again and again.

Page 4: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

Features of the Dataset - Labelled 699 clinical cases

Nine real-valued features are chosen for each cell nucleus:a) radius (mean of distances from center to points on the perimeter)b) texture (standard deviation of gray-scale values)c) perimeterd) areae) smoothness (local variation in radius lengths)f) compactness (perimeter^2 / area - 1.0)g) concavity (severity of concave portions of the contour)h) concave points (number of concave portions of the contour)i) symmetry

Page 5: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

Some approaches to solving anomaly detection problem

Page 6: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

683 cases of labelled data - benign/malignant

Imbalanced dataset

Page 7: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

Spot check feature histograms

Page 8: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

Visualizing Classification Classification using Logistic Regression achieved an F1 score of 0.95 on the anomaly class.

Classification by model on unseen data

Actual data

Page 9: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

KNN - non parametric model to verify classification

Scaled feature vector

Identified precise k value using elbow methodFor k = 3We had an F1 score 0.95 for the anomaly class

Page 10: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

Unsupervised Anomaly Detection using SVM - Gaussian Kernel Trick

1)Objective is to train a one class svm gaussian hypersphere that quarantines the benign cells.

2)Dropped labels from dataset and is split into benign and malignant datasets.

3)Benign dataset is used to train the model.

4)Malignant dataset, the dataset that contains the outliers is used to test.

5)A single class SVM is trained with a low gamma value, that captures the influence of training examples on classification.

Page 11: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

Gaussian Distribution for benign and malignant cells

Benign multivariate gaussian Malignant multivariate gaussian

Page 12: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

Page 13: Anomaly Detection using SIngle Class SVM with Gaussian Kernel

Single class SVM with gaussian trick - 100% Accuracy

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Anomaly Detection using SIngle Class SVM with Gaussian Kernel

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