This document describes a method to improve breast cancer diagnosis using machine learning techniques. It discusses causes of medical errors and describes a dataset containing features of cell nuclei used to train classification models. Logistic regression and KNN models achieved F1 scores of 0.95 for identifying anomalous cases. An unsupervised anomaly detection method using a one-class SVM with a Gaussian kernel was also able to distinguish benign from malignant cells in the test dataset with 100% accuracy.

1. Method to Improve Breast
Cancer Diagnosis
Anoop Vasant Kumar

2. Problem
Preventable 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.

3. Causes of Avoidable Medical Errors
Procedures and training methods not reformed,
so mistakes happen again and again.

4. 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) perimeter
d) area
e) 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

5. Some approaches to solving anomaly detection problem

6. 683 cases of labelled data - benign/malignant
Imbalanced dataset

7. Spot check feature histograms

8. 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

9. KNN - non parametric model to verify classification
Scaled feature vector
Identified precise k value using elbow method
For k = 3
We had an F1 score
0.95 for the anomaly
class

10. 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.

11. Gaussian Distribution for benign and malignant cells
Benign multivariate gaussian Malignant multivariate
gaussian

13. Single class SVM with gaussian trick - 100% Accuracy