Breast Cancer Analysis using ML

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Inspiration

The theme of the event, 'health', got us thinking of how useful the advent of Machine Learning and AI is and how it's going to influence our lives in the future. Atleast 1 out of 8 women in the US contract invasive breast cancer over the course of their lives. These stats are shocking and is the motive of starting this project, to make things a little easier for the healthcare department

What it does

Our program takes breast cancer dataset to analyze the features and attributes of the tumor cells, ultimately classifying if the tumor is malignant or benign. We implemented 2 separate algorithms to give us atleast 98% accuracy of predicting the tumor

How we built it

We used Python along with Scikit Learn package to implement the program. Panda was used to structure dataframes and datasets

Challenges we ran into

We ran into problems with accuracy of our prediction model. The challenge was to use powerful algorithms but also making sure that it did not take up too much runtime. It was also important to make our code readable so that healthcare professionals easily understand and implement the code

Accomplishments that we're proud of

We got an accuracy of 98% which is really close to perfect. We definitely want to improve our algorithm further to make it 99 or even 100% accurate

What we learned

We learned how important data collection and manipulation is. Also, it was mind boggling to see how much ML and AI can be used in every sphere of life

What's next for Breast Cancer Analysis using ML

We want to convert this into an executable environment so that healthcare professionals just have to upload the data and be able to predict results. We would also like to implement image processing so that we can extract feature details from mammograms in the future.

Built With

• ai
• ml
• python
• scikit-learn