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Ryan Ahmed
In this hands-on project, we will build and train an XG-Boost classifier to predict whether a person has a risk of having cervical cancer. Cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide.
Data has been obtained from 858 patients and include features such as number of pregnancies, smoking habits, Sexually Transmitted Disease (STD), demographics, and historic medical records.
Register for this course and see more details by visiting:
OpenCourser.com/course/e5qbyu/cervical
What's inside
Syllabus
Cervical Cancer Risk Prediction Using Machine Learning
In this hands-on project, we will build and train an XG-Boost classifier to predict whether a person has a risk of having cervical cancer. Cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide.
Data has been obtained from 858 patients and include features such as number of pregnancies, smoking habits, Sexually Transmitted Disease (STD), demographics, and historic medical records. Results have shown that High sexual activity Human papilloma virus (HPV) is one of the key factors that increases the risk of having cervical cancer. The presence of hormones in oral contraceptives, having many children, and smoking increase the risk for developing cervical cancer, particularly in women infected with HPV. Also, people with weak immune systems (HIV/AIDS) have high risk of HPV.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores cervical cancer risk prediction using machine learning, a topic relevant to healthcare professionals and researchers
Uses real-world data to train the XG-Boost classifier, providing learners with practical insights
Suitable for individuals with an interest in healthcare, machine learning, or data analysis looking to enhance their skills
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Reviews summary
Applicable and interesting course
Learners say that Cervical Cancer Risk Prediction Using Machine Learning is an applicable course that is interesting.
Uses new software for data analysis.
"Data analysis with a new software"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Cervical Cancer Risk Prediction Using Machine Learning with these
activities:
Review the basics of machine learning
Show steps
Reviewing the basics of machine learning will help you understand the concepts and algorithms used in this course, making the learning process smoother.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Read introductory articles or tutorials on machine learning
-
Watch videos or take introductory courses on machine learning
-
Practice solving simple machine learning problems on platforms like Kaggle or LeetCode
Form a study group with other students
Show steps
Forming a study group will provide you with opportunities to discuss course material, ask questions, and learn from your peers.
Show steps
-
Identify potential group members
-
Set up regular meeting times
-
Prepare and discuss topics together
Follow tutorials on XG-Boost
Show steps
Following tutorials on XG-Boost will provide you with hands-on experience in using the algorithm, which will be beneficial for completing the course projects.
Browse courses on
XG-Boost
Show steps
-
Find tutorials on XG-Boost from reputable sources
-
Follow the tutorials step-by-step
-
Implement the XG-Boost algorithm in your own code
-
Test your implementation on sample datasets
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a workshop on machine learning for healthcare
Show steps
Attending a workshop on machine learning for healthcare will provide you with insights from experts and allow you to connect with other professionals in the field.
Browse courses on
Machine Learning
Show steps
-
Find and register for a relevant workshop
-
Attend the workshop and actively participate in discussions
-
Connect with other attendees and speakers
Solve practice problems on cervical cancer prediction
Show steps
Solving practice problems on cervical cancer prediction will help you apply the concepts and techniques learned in the course to a real-world problem.
Show steps
-
Find practice problems or datasets related to cervical cancer prediction
-
Use the XG-Boost algorithm to solve the problems
-
Evaluate your results and identify areas for improvement
Compile resources on cervical cancer and machine learning
Show steps
Compiling resources on cervical cancer and machine learning will help you stay organized and have easy access to relevant materials for further learning.
Show steps
-
Identify and collect resources from various sources
-
Organize the resources into categories or folders
-
Create a reference list or bibliography
Build a cervical cancer prediction model
Show steps
Building a cervical cancer prediction model will allow you to apply your knowledge and skills to a practical project, enhancing your understanding of the course material.
Show steps
-
Gather data and preprocess it
-
Select and train an appropriate machine learning model
-
Evaluate the performance of your model
-
Write a report documenting your findings
Contribute to an open-source project related to cervical cancer prediction
Show steps
Contributing to an open-source project will allow you to collaborate with others, gain practical experience, and enhance your skills in cervical cancer prediction.
Show steps
-
Find an open-source project related to cervical cancer prediction
-
Identify areas where you can contribute
-
Follow the project's guidelines and contribute code or documentation
Review the basics of machine learning
Show steps
Reviewing the basics of machine learning will help you understand the concepts and algorithms used in this course, making the learning process smoother.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Read introductory articles or tutorials on machine learning
- Watch videos or take introductory courses on machine learning
- Practice solving simple machine learning problems on platforms like Kaggle or LeetCode
Form a study group with other students
Show steps
Forming a study group will provide you with opportunities to discuss course material, ask questions, and learn from your peers.
Show steps
Show steps
Show steps
- Identify potential group members
- Set up regular meeting times
- Prepare and discuss topics together
Follow tutorials on XG-Boost
Show steps
Following tutorials on XG-Boost will provide you with hands-on experience in using the algorithm, which will be beneficial for completing the course projects.
Browse courses on
XG-Boost
Show steps
Show steps
Show steps
- Find tutorials on XG-Boost from reputable sources
- Follow the tutorials step-by-step
- Implement the XG-Boost algorithm in your own code
- Test your implementation on sample datasets
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a workshop on machine learning for healthcare
Show steps
Attending a workshop on machine learning for healthcare will provide you with insights from experts and allow you to connect with other professionals in the field.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find and register for a relevant workshop
- Attend the workshop and actively participate in discussions
- Connect with other attendees and speakers
Solve practice problems on cervical cancer prediction
Show steps
Solving practice problems on cervical cancer prediction will help you apply the concepts and techniques learned in the course to a real-world problem.
Show steps
Show steps
Show steps
- Find practice problems or datasets related to cervical cancer prediction
- Use the XG-Boost algorithm to solve the problems
- Evaluate your results and identify areas for improvement
Compile resources on cervical cancer and machine learning
Show steps
Compiling resources on cervical cancer and machine learning will help you stay organized and have easy access to relevant materials for further learning.
Show steps
Show steps
Show steps
- Identify and collect resources from various sources
- Organize the resources into categories or folders
- Create a reference list or bibliography
Build a cervical cancer prediction model
Show steps
Building a cervical cancer prediction model will allow you to apply your knowledge and skills to a practical project, enhancing your understanding of the course material.
Show steps
Show steps
Show steps
- Gather data and preprocess it
- Select and train an appropriate machine learning model
- Evaluate the performance of your model
- Write a report documenting your findings
Contribute to an open-source project related to cervical cancer prediction
Show steps
Contributing to an open-source project will allow you to collaborate with others, gain practical experience, and enhance your skills in cervical cancer prediction.
Show steps
Show steps
Show steps
- Find an open-source project related to cervical cancer prediction
- Identify areas where you can contribute
- Follow the project's guidelines and contribute code or documentation
Career center
Learners who complete Cervical Cancer Risk Prediction Using Machine Learning will develop knowledge and skills
that may be useful to these careers:
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts use data to improve the quality of healthcare. They work with doctors, nurses, and other healthcare professionals to identify trends, patterns, and insights in data. This information can be used to improve patient care, reduce costs, and make better decisions about healthcare policy. The Cervical Cancer Risk Prediction Using Machine Learning course can help you learn the skills you need to become a successful Healthcare Data Analyst. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Biostatistician
Biostatistician
Biostatisticians use statistics to solve problems in biology and medicine. They work with scientists and researchers to design studies, collect data, and analyze results. Biostatisticians also develop statistical models to help scientists understand the data they have collected. The Cervical Cancer Risk Prediction Using Machine Learning course can help you develop the skills needed to become a successful Biostatistician. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing, deploying, and maintaining machine learning models. They work closely with Data Scientists to ensure that models are accurate and efficient. Machine Learning Engineers also work with software engineers to integrate machine learning models into applications. The Cervical Cancer Risk Prediction Using Machine Learning course can help you develop the skills needed to become a successful Machine Learning Engineer. The course will teach you how to build and train machine learning models, as well as how to deploy and maintain these models in production.
Public Health Analyst
Public Health Analyst
Public Health Analysts use data to improve the health of populations. They work with public health officials to identify and address health problems in communities. Public Health Analysts use a variety of methods to conduct their research, including surveys, interviews, and data analysis. The Cervical Cancer Risk Prediction Using Machine Learning course can help you develop the skills needed to become a successful Public Health Analyst. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Clinical Research Coordinator
Clinical Research Coordinator
Clinical Research Coordinators manage the day-to-day operations of clinical trials. They work with investigators to develop and implement study protocols, and they oversee the recruitment and enrollment of patients. Clinical Research Coordinators also collect and manage data, and they ensure that the study is conducted in accordance with ethical and regulatory guidelines. The Cervical Cancer Risk Prediction Using Machine Learning course can help you develop the skills needed to become a successful Clinical Research Coordinator. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Data Scientist
Data Scientist
Data Scientists take a variety of large and complex datasets and create models to help businesses understand the data they have better. This can be used for tasks such as predicting customer behavior, identifying fraud, and optimizing marketing campaigns. Data Scientists use a variety of techniques, including machine learning, statistics, and data visualization. The Cervical Cancer Risk Prediction Using Machine Learning course can help you develop the skills needed to become a successful Data Scientist by teaching you how to use machine learning to build predictive models using real world data.
Health Educator
Health Educator
Health Educators develop and implement programs to promote health and prevent disease. They work with individuals, groups, and communities to provide information about health topics and to encourage healthy behaviors. The Cervical Cancer Risk Prediction Using Machine Learning course can help you develop the skills needed to become a successful Health Educator. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease and other health problems in populations. They work with public health officials to develop and implement programs to prevent and control diseases. Epidemiologists use a variety of methods to conduct their research, including surveys, interviews, and data analysis. The Cervical Cancer Risk Prediction Using Machine Learning course can help you develop the skills needed to become a successful Epidemiologist. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Healthcare Administrator
Healthcare Administrator
Healthcare Administrators plan, organize, and manage healthcare organizations. They work with doctors, nurses, and other healthcare professionals to ensure that patients receive the best possible care. Healthcare Administrators also work with insurance companies and government agencies to ensure that healthcare is accessible and affordable. The Cervical Cancer Risk Prediction Using Machine Learning course can help you develop the skills needed to become a successful Healthcare Administrator. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Health Policy Analyst
Health Policy Analyst
Health Policy Analysts develop and analyze policies that affect the health of populations. They work with policymakers, public health officials, and other stakeholders to ensure that health policy is based on sound evidence. The Cervical Cancer Risk Prediction Using Machine Learning course can help you develop the skills needed to become a successful Health Policy Analyst. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Medical Researcher
Medical Researcher
Medical Researchers conduct research to improve the understanding, prevention, and treatment of diseases. They work in a variety of settings, including universities, hospitals, and research institutes. Medical Researchers use a variety of methods to conduct their research, including clinical trials, laboratory studies, and data analysis. The Cervical Cancer Risk Prediction Using Machine Learning course can help you develop the skills needed to become a successful Medical Researcher. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Medical Writer
Medical Writer
Medical Writers create and edit written materials about medical topics. They work with healthcare professionals, patients, and the public to provide accurate and understandable information about diseases, treatments, and health policy. The Cervical Cancer Risk Prediction Using Machine Learning course can help you develop the skills needed to become a successful Medical Writer. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Pharmacist
Pharmacist
Pharmacists dispense medications and provide advice on their use. They work with doctors and other healthcare professionals to ensure that patients are taking their medications safely and effectively. Pharmacists also provide information about drug interactions and side effects. The Cervical Cancer Risk Prediction Using Machine Learning course may be useful for Pharmacists who want to learn more about how machine learning can be used to improve patient care. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Nurse Practitioner
Nurse Practitioner
Nurse Practitioners provide primary and specialty care to patients. They work with doctors and other healthcare professionals to diagnose and treat illnesses and injuries. Nurse Practitioners also provide preventive care and health education. The Cervical Cancer Risk Prediction Using Machine Learning course may be useful for Nurse Practitioners who want to learn more about how machine learning can be used to improve patient care. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
Physician Assistant
Physician Assistant
Physician Assistants provide primary and specialty care to patients under the supervision of a physician. They work with doctors and other healthcare professionals to diagnose and treat illnesses and injuries. Physician Assistants also provide preventive care and health education. The Cervical Cancer Risk Prediction Using Machine Learning course may be useful for Physician Assistants who want to learn more about how machine learning can be used to improve patient care. The course will teach you how to use machine learning to build predictive models that can be used to identify patients at risk of developing cervical cancer.
For more career information including salaries, visit:
OpenCourser.com/course/e5qbyu/cervical
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Cervical Cancer Risk Prediction Using Machine Learning.
Pocket guide to cervical cancer screening, providing up-to-date information on the risks of cervical cancer, the benefits of screening, and the different screening tests available. It useful resource for women who are considering getting screened for cervical cancer.
Save
Guide for clinicians on HPV, covering the latest information on the virus, its transmission, and its role in cervical cancer development. It useful resource for clinicians who care for patients who are at risk for HPV or cervical cancer.
Guide to the ASCCP 2012 Revised Bethesda System for Reporting Cervical Cytology, a system used to classify cervical cell samples. It useful resource for clinicians who interpret cervical cytology reports.
Provides evidence-based guidelines and recommendations for cervical cancer screening programs, including best practices and quality assurance.
This textbook provides comprehensive information on all aspects of internal medicine, including cervical cancer.
This online resource provides up-to-date information on all aspects of medicine, including cervical cancer.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/e5qbyu/cervical
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Explores cervical cancer risk prediction using machine learning, a topic relevant to healthcare professionals and researchers
Uses real-world data to train the XG-Boost classifier, providing learners with practical insights
Suitable for individuals with an interest in healthcare, machine learning, or data analysis looking to enhance their skills
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