– Classification of Breast Cancer using Logistic Regression –
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ABSTRACT
Breast cancer is a prevalent disease that affects mostly women, an early diagnosis will expedite the treatment of this ailment. In recent times, Machine Learning (ML) techniques have been employed in biomedical and informatics to help fight breast cancer.
This research work proposed an ML model for the classification of breast cancer. To achieve this we employed logistic regression (LR) and also compared our model’s performance with other extant ML models namely, Support Vector Machine (SVM), Naïve Bayes (NB), and Multilayer Perceptron (MLP).
The original Wisconsin Diagnostic Breast Cancer dataset (WDBC) was used. Our performance evaluation was done for two phases, i.e.
Phase 1: when the WBCD is scaled (feature scaling) and Phase 2: when the dataset is not scaled. All models excluding MLP performed well when there is no feature scaling of dataset with f1-scores of (LR=97%, SVM = 97%, NB = 95%, MLP= 52%).
However, when feature scaling is applied on dataset, the four models have f1-scores above 90% (SVM = 98%, LR = 97%, NB = 97%, MLP = 97%).
Notably, the f1-score for LR in both cases did not change, hence to the best of our knowledge, we concluded that LR, given its simplicity and low time complexity is a good model to employ for binomial classification.
TABLE OF CONTENTS
CERTIFICATION ……. i
Dedication ……………. v
Acknowledgement ……. vi
Table of content ………….. vii
List of Table ……. ix
List of figures ………….. x
List of Abbreviations ………. xi
CHAPTER ONE INTRODUCTION
1.1 Research Background …………. 1
1.1.1 Data Mining …………… 2
1.1.2 Classification ……….. 3
1.2 Problem statement …. 3
1.3 Research Aim and objectives …….. 3
1.4 Limitation of study …… 3
1.5 Paper organization ………. 4
CHAPTER TWO LITERATURE REVIEW
2.1 Basic Terminologies and Concepts ………. 5
2.1.1 Data Pre-processing ……………. 6
2.1.2 Feature scaling ……… 6
2.1.3 Supervised Learning ……… 6
2.1.4 Classification ……………. 7
2.2 Literature Review ……….. 8
CHAPTER THREE MATERIALS AND METHOD
3.1 Concept of Classification Technique …………… 12
3.2 Software Design Phase ……. 12
3.3 Hardware Requirement ……… 13
3.4 Proposed Framework …… 13
3.4.1 Experiments ……… 14
3.4.2 Data collection ……. 15
3.4.2 Data pre-processing …………… 18
3.4.3 Machine learning classifiers …… 19
3.5 Classifier Performance Evaluation Criteria … 26
3.5.1 Confusion matrix …….. 26
3.5.2 Precision … 27
3.5.3 Recall (Sensitivity) …….. 27
3.5.4 F1-Score ….. 28
CHAPTER FOUR RESULTS AND DISCUSSIONS
4.1 Presentation of Results .. 29
4.1.1 Reading the Textual File …… 29
4.1.2 Data pre-processing ………. 30
4.1.3 Training of classifiers and classification task …. 32
4.1.4 Performance Analysis ………………… 35
4.2 Our Contribution………. 40
CHAPTER FIVE SUMMARY, CONCLUSION AND FUTURE WORK
5.1 Summary …. 41
5.2 Conclusion ………… 42
5.3 Future work ……. 42
INTRODUCTION
1.1 Research Background
Breast cancer is now one of the most prevailing cancers that affects humans, especially woman, and early diagnosis would go a long way to reducing the damage done by this cancer on its victims.
Breast cancer’s causes are multifactorial and involve family history, obesity, hormones, radiation therapy, and even reproductive factors.
Every year, one million women are newly diagnosed with breast cancer, according to the report of the world health organization half of them would die, because it’s usually late when doctors detect the cancer (Aaltonen et al., 1998).
Breast cancer can be categorized into two, which are malignant breast cancer and benign breast cancer. The classification of breast cancer as either malignant or benign is possible by scientifically studying the features of breast tumours, lumps, or any abnormalities found in the breast.
At the benign stage the cancer has less risk and is not life- threatening while cancer that is categorized as malignant is life-threatening (Huang, Chen, Lin, Ke, & Tsai, 2017).
Malignant tumours expand to the neighbouring cells, which can spread to other parts, whereas benign masses can’t expand to other tissues, the expansion is then only limited to the benign mass (Aaltonen et al., 1998; Huang et al., 2017).
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