Problem

Medical diagnosis is a critical task that requires accuracy and precision. Breast cancer is one of the most common types of cancer in women, and early detection can significantly improve the chances of successful treatment. However, manual diagnosis can be time-consuming and prone to errors. Therefore, there is a need for an automated system that can predict breast cancer diagnosis based on cell nucleus features.

Our team was tasked with developing such a system using logistic regression. The challenge was to preprocess the data, train the model, and evaluate its performance using various metrics while ensuring high accuracy and reliability.

Link to relevant project

Solution

We started by collecting a dataset containing cell nucleus features from patients diagnosed with breast cancer. We then preprocessed the data by normalizing it and splitting it into training and testing sets.

Next, we trained a logistic regression model on the training set using gradient descent optimization. We fine-tuned the hyperparameters to achieve optimal performance on the testing set while avoiding overfitting. Finally, we evaluated the model's performance using accuracy, precision, recall, and F1-score metrics.

Impact

Improved breast cancer diagnosis accuracy.
Reduced time required for diagnosis.

Team

Rushikesh Choudhari- Data Scientist
- Preprocessed the data and trained the logistic regression model
- Fine-tuned the hyperparameters to optimize performance
Rushikesh Choudhari - Machine Learning Engineer
- Evaluated the model's performance using various metrics
- Implemented gradient descent optimization for training the model

Automated Breast Cancer Diagnosis System