## Contact

Discord: itssujee#9904

Team Number: 65

## Inspiration

During the COVID-19 pandemic hospitals have been over-working their staff and as a result quality of care has taken a hit. One of the common ailments associated with contracting COVID-19 is pneumonia, which is a deadly combination.

## What it does

The 50 Shades of Grey Project uses a Machine Learning Model to determine whether or not an X-ray shows symptoms of pneumonia. Much of the backend processing is done through DCP, which allows for quick distributed cloud processing.

## How I built it

The dataset was hosted on the Google Cloud Platform in the GCS bucket. I made the dataset public to allow for easy access in my notebook. I found a couple articles, research papers, and official Tensorflow documentation to create the Machine Learning Model. I used the DCP API to distribute the work required to process the data for the machine learning model to digest it.

## Challenges I ran into

The DCP API does not support a lot of features that data scientists would find useful. Furthermore the platform is in a extremely beta form and the lack of documentation made the discovery process more difficult.

## Accomplishments that I'm proud of

I'm proud that my model achieved a 98% recall rate, and a 70% accuracy on the testing dataset.

## What I learned

I learned how to data engineer using parallelization, as well as I figured out how to build, train, and export a machine learning model in Tensorflow.

## What's next for 50 Shades of Grey

Developing better support for parallelization for data scientists in conjunction with the DCP Platform.
## 💡 Inspiration 💡

So many people around the world are fatally injured and require admission to multiple hospitals in order to receive life-changing surgery/procedures. When patients are transferred from one hospital to another, it is crucial for their medical information to be safely transferred as well.

## ❓ What it does ❓

Hermes is a secure, HIPAA-compliant app that allows hospital admin to transfer vital patient data to other domestic and/or international hospitals. The user inputs patient data, uploads patient files, and sending them securely to a hospital.

## ⚙️ How we built it ⚙️

We used the React.JS framework to build the web application. We used Javascript for the backend and HTML/CSS/JS for the frontend. We called the Auth0 API for authentication and Botdoc API for encrypted file sending.

## 🚧 Challenges we ran into 🚧

Figuring out how to send encrypted files through Botdoc was challenging but also critical to our project.

## ✨Accomplishments that we're proud of ✨

We’re proud to have built a dashboard-like functionality within 24 hours.

## 👩🏻‍💻 What we learned 👩🏻‍💻

We learned that authentication on every page is critical for an app like this that would require uploaded patient information from hospital admins. Learning to use Botdoc was also fruitful when it comes to sending encrypted messages/files.
## Inspiration

With an emerging interest for Machine Learning and neural networks in Healthcare and Medicine I wanted to build something meaningful and applicable to the real world. I wanted to work with one of the popular UC Irvine Machine Learning Repository data sets.

## What it does

The model essentially takes the Breast Cancer data-set and performs some basic mathematical operations to predict if the tumours are malignant or benign. I used the two-class logistics regression algorithm as the activation function for the hidden layer. After the operation, I implemented scoring and evaluation of the model as well. The model has an approximate accuracy of 96%.

## How I built it

I built the model using Microsoft Azure Machine Learning Studio. I had the opportunity to utilize the statistical functions, Machine Learning (Initialization, scoring, and evaluation), data transformation and data set features. I approached the development of the model in two large sections:

Part 1: Data Clean Up

* Imported data set from UC Irvine Machine Learning Repository onto Azure
* Modified the data set (removed certain elements like the ID row from CSV)
* Utilized math operations from statistical functions to remove rows with missing / invalid data

Part 2: Training

* Normalized the data-set (Z-score) and split into a training set and data set
* Initialized the model using the two-class logistics regression to deal with the issue of classification (malignant vs benign)
* Scored and evaluated the model in comparison to data set

## Challenges I ran into

I had difficulty figuring out which algorithm would work best as an activation function for training the data set. Additionally, splitting the data set and developing a work around was a little tricky in Azure.

## Accomplishments that I'm proud of

I am proud of finally gaining a better understanding of neural networks and their overall development. I am also quite pleased to have had my network model fully run / compile on Azure and publish it in my gallery.

## What I learned

I learned to use Microsoft Azure and the plethora of features it includes that are very useful for modelling complex problems. I found it was quite easy to work with especially when there are difficulties in code implementation. Microsoft Azure made it easy to get my idea done with limited knowledge of writing out complex algorithms.

## What's next for Neural Network: Breast Cancer Model

I would like to write out the results obtained from the model into a CSV file. Essentially, extending the final results in a more viewable format, that is easier to work with and analyze.