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
  • 2 bar graph
    Difficulty: Intermediate

    Foundational knowledge or experience in statistics or analytics is recommended.

  • Asset 1
    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


Lifetime Access

Access this project for life once completed


Flexible Scheduling

Start learning online immediately, at your own pace


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.

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