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Detection of Breast Cancer in a Clinical Trial
This project is essentially designed to help learners apply various EDA and Classification techniques to detect breast cancer.
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Difficulty: IntermediateFoundational knowledge or experience in statistics or analytics is recommended.
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Duration: Approximately 2 hours
Case Overview
- Machine learning techniques are important when generating results for accurate predictions and preventing future occurence of unwanted events.
- Medical diagnosis is a serious affair. More accurate detection of a life threatening disease like cancer goes a long way in saving lives. Breast cancer prognosis has always been considered a critical area of investigation in the healthcare and medical sector.
- To diagnose medical concerns various visualization techniques and models can be built for more accurate assessment of a life-threatening condition like cancer which can save a lot of lives.
- Learn to build prediction models using machine learning techniques to help improve your diagnosis.
What’s included
Flexible Scheduling
Start learning online immediately, at your own pace
Desktop Only
We recommend completing this project on a desktop
Skills You Will Learn
Associated Learning Tasks
- Breast cancer prognosis has always been considered a critical area of investigation in the healthcare and medical sector, thus it is necessary to determine dependent and independent indicators.
- The primary obstacle to identification is recognizing whether a tumor is aggressive (cancerous) or harmless (non-cancerous).
- It is indeed extremely important to have a platform that supports early identification and prevention, hence increasing breast cancer survival rates.
- Firstly, the data is summarized and visually represented to determine correlation among different indicators like tumor size, thickness etc.
- VIF analysis was also done to reveal various trends and independent indicators whose presence has a significant impact of the target variable.
- Classification model was built using Support Vector Machine technique.
- The case can be utilized by the healthcare department to develop a predictive model.
- The model can be used to predict whether the patient will receive a positive breast cancer diagnosis based on a variety of tumor characteristics.
- Sophisticated models provide accurate results which can save lives.
How it Works
Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.
With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.
Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.
Complete courses to earn sharable certificates and badges. These awarded items will be look great in your portfolio as you showcase your skills and project experience to employers and colleagues.
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Rolai’s patented process provides a personalized learning process for each user. Rolai goes deeper than simply learning concepts and testing your skills. At Rolai, learners can apply their skills to actual industry use cases and projects.
Our courses include readings, videos, quizzes, and hands-on data cases that are completed using our virtual lab; give learners an applied learning experience.
No additional tools are needed to begin learning with Rolai. Our virtual lab contains the necessary data workspace and an embedded coding console.
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