Cervical cancer is the leading gynecological malignancy worldwide. This paper presents diverse classification techniques and shows the advantage of feature selection approaches to the best predicting of cervical cancer disease. There are thirty-two attributes with eight hundred and fifty-eight samples. Besides, this data suffers from missing values and imbalance data. Therefore, over-sampling, under-sampling and embedded over and under sampling have been used. Furthermore, dimensionality reduction techniques are required for improving the accuracy of the classifier. Therefore, feature selection methods have been studied as they divided into two distinct categories, filters and wrappers. The results show that age, first sexual intercourse, number of pregnancies, smokes, hormonal contraceptives, and STDs: genital herpes are the main predictive features with high accuracy with 97.5%. Decision Tree classifier is shown to be advantageous in handling classification assignment with excellent performance.

1. Presenter
Avishek Choudhury
May 21-2018
Classification of Cervical Cancer Dataset
1

2. AGENDA
• Introduction.
• Problem statement
• Methodology
• Results
• Conclusion
2

3. INTRODUCTION
3
• Cervical cancer is the most common
cancer among women in developing
countries.
• In 2015 about 129,000 cases of cervical
cancer were recorded within USA.
• According to WHO 273,000 people died
due to cervical cancer worldwide.

4. PROBLEM STATEMENT
To develop a prediction model
to identify the relevant factors
affecting cervical cancer.
To highlight the importance of
data pre-processing and
feature selection.

5. 5
• 858 samples
• 32 features
• 4 classes (Hinselmann, Schiller,
Cytology and Biopsy)
• Biopsy ~ target
Source: Fernandes, K., J.S. Cardoso, and J. Fernandes., 2017,” Transfer
Learning with Partial Observability Applied to Cervical Cancer Screening” In
Iberian Conference on Pattern Recognition and Image Analysis, Springer.
DATA DESCRIPTION

6. DATA DESCRIPTION
6
Attribute Type Attribute Type Attribute Type
Age Integer STDs Bool STDs:HIV Bool
Number of sexual
partners
Integer STDs (number) Integer STDs:Hepatitis B Bool
First sexual intercourse
(age)
Integer STDs:condylomatosis Bool STDs:HPV Bool
Number of pregnancies Integer STDs:cervical condylomatosis Bool STDs: Number of
diagnosis
Integer
Smokes Bool STDs:vaginal condylomatosis Bool STDs: Time since first
diagnosis
Integer
Smokes (years) Bool STDs:vulvo-perineal
condylomatosis
Bool STDs: Time since last
diagnosis
Integer
Smokes (packs/year) Bool STDs:syphilis Bool Dx:Cancer Bool
Hormonal
Contraceptives
Bool STDs:pelvic inflammatory
disease
Bool Dx:CIN Bool
Hormonal
Contraceptives (years)
Integer STDs:genital herpes Bool Dx:HPV Bool
IUD Bool STDs:molluscum contagiosum Bool Dx Bool
IUD (years) Integer STDs:AIDS Bool

7. METHODOLOGY
1. Feature Selection 4. Prediction Models
2. Impute missing
values
3. Balancing Data
Methodology

8. Performance Measures

9. IMBALANCED DATA

10. Data Balancing
Under
Both
Over

11. 11
Balanced Data

12. 12
FEATURE
SELECTION

13. SEQUENTIAL BACKWARD
SELECTION

14. SEQUENTIAL FORWARD
SELECTION

15. FEATURE SELECTION
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16. CONCLUSION
Decision Tree
Pre-processing & Feature selection
Age, First Sexual Intercourse, Number of Pregnancies,
Smokes, Hormonal Contraceptives and STDs: Genital
Herpes

17. LIMITATIONS
Specific
Overfit
Better techniques

18. 18