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.

Detection of Breast Cancer in A Clinical Trial - Application Of SVM
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    Difficulty: Intermediate

    Foundational 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

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Lifetime Access

Access this project for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Data Management

Data Visualization

Feature Engineering

Model Evaluation

Decision Tree Regressor

Machine Learning

Associated Learning Tasks

Case Context

  • 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

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