Increasing Cervical Cancer Risk Analysis

Cervical Cancer is an increasing matter that is affecting various women across the nation, in this project we will be analyzing risk factors that are producing higher chances of this cancer. In order to analyize these risk factors a machine learning technique is implemented to help us understand the leading factors of cervical cancer.

Check Report Status Status: draft, Type: Project

Theresa Jean-Baptistee, su21-reu-369, Edit

Abstract

Cervical Cancer is an increasing matter that is affecting various women across the nation, in this project we will be analyzing risk factors that are producing higher chances of this cancer. In order to analyize these risk factors a machine learning technique is implemented to help us understand the leading factors of cervical cancer.

Contents

Keywords: Cervical, Cancer, Diseases, Data, conditions

1. Introduction

Cervical cancer is a disease that is increasing in various women nationwide. It occurs within the cells of the cervix (can be seen in stage 1 of the image below). This cancer is the fourth leading cancer, where there are about 52,800 cases found each year, predominantly being in lower developed countries. Cervical cancer occurs most commonly in women who are within their 50’s and who has symptoms such as watery and bloody discharge, bleeding, and painful intercourse. Two other common causes can be an early start on sexual activity and multiple partners. The most common way to determine if one may be affected by this disease is through a pap smear. When witnessed early it, can allow a better chance of results and treatment.

Cervical cancer is so important for the future of reproduction, being the cause of a successful or unsuccessful birth with completions like premature a child. The cervix help keeps the fetus stable within the uterus during this cycle, towards the end of development, it softens and dilates for the birth of a child. If diagnosed with this cancer, a miracle would be needed to conceive a child after having treatment. Most treatments begin with a biopsy removing affected areas of cervical tissue. As it continues, to spread radiotherapy might be recommended to treat the cancer where may affect the womb. lastly, one may need to have a hysterectomy which is the removal of the womb.

In this paper, we will study the exact cause and risk factors that may place someone in this position. If spotted early it wouldn’t affect someone’s dream chance of conceiving or affect their reproductive parts. Using various data sets we will study the way everything may alignes in causes and machine leaning would be the primary technique to used interpretate the relation between variables and risk factor on cervical cancer.

Figure 2

Model

Figure 1

2. DataSets

The Data sets obatained shows the primary risk factors that affect women ages 15 and above. The few factors that sticked out the most were age, start of sexual activity, tabacoo intake, and IUD. The age and start of sexual activity maybe primary factor because a person is more liable to catch an STD and get this diease from mutiple parnters never really knowing what the other person may be doing outside of the encounterment. Tabcoo intake causes an affect making a person by weaking the immune system and making somone more septable to the disease. The IUD has the highest number on the data set being a primary factor that may put a person at risk, this device aids the prevention of pregency by thickneing the mucos of the cervix that could later cause infection or make your more spetiable to them.

IUD Visulaization

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Tabacoo Visulization Affect On Cervixs

Figure 3

Correlation of Age and Start Of sexual activity

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3. Other People Works

The research of others work has made a huge imapact to this project starting from data to important knowledge needed to conduct the project. With the various research sites, we were able to witness what the affects various day to day activtie affect women long term. The Cervical Cancer Diagnosis Using a Chicken Swarm Optimization Based Machine Learning Method, was a big aid throught the project explaing the stages of cervical cancer, ways it can be treated, and the affects it may cause. With the data that was used from UCI Machine Learning, we were able to find efficent correlation into the data, helping the implented machine learning algorithm for the classification task.

4. Explantion of Confusion Matrix

The confusion matrix generated by multilayer perceptron can be explained as the perdicted summary results from the data obtained. Zero is when no cervical cancer is witnessed, one is when cervical cancer is seen. A hundrend and sixty-two is the highest number of this disease seen on the chart and the lowest number being winessed is two and eight being quiet of a jump.

5. Benchmark

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6. Conclusion

In conclusion it can be found as women partake in their first sexual activity and continue they are more at risk. 162 is a dramatic number not necessarily being affected by age and 0 is only seen when a person does not partake in it. In the future I hope to keep furthering my Knowledge on Cervical Cancer, hopefully coming up with a realistic method to cure this disease where one can continue to live their life with as a human being.

7. Acknowledgments

The author would like to thank Yohn, Carlos, Gregor, Victor, and Jacques for all of their Help. Thank you!

8. References