Patient-Survival - Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have undergone breast cancer surgery.

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Deep Learning Patient-Survival
Overview

Patient-Survival

Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have undergone breast cancer surgery. I then evaluated both models' performances to shortlist the better one.

The dataset contains cases from a study that was conducted between 1958 and 1970 on the survival of patients who had undergone surgery for breast cancer. • Number of instances: 306 • Number of attributes: 4 (including the class attribute) • Attribute Information:

  1. Age of patient at time of operation (numerical)
  2. Patient’s year of operation (year -1900, numerical)
  3. Number of positive axillary nodes detected (numerical)
  4. Survival status (class attribute)  1 = the patient survived 5 years or longer  2 = the patient died within 5 years
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