blueml.html

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							<h3>BlueML</h3>
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								<h4>Overview</h4>
								<p><span class="image left"><img src="images/pic01.jpg" alt="" /></span><p>Back in Fall of 2017, I joined BlueML when this open source project was barely a year old. Not only was I was intrigued by machine learning and web development, I felt that my work would actually be fulfilling as we have a direct impact on people in need by diagnosing cardiac and neurological disorders. Essentially the end goal would be for users to be able to enter EKG or EEG data, select features for classification, and receive an assessment with a high percentage of accuracy. </p>

								<h4>My Contributions</h4>
								<p><span class="image right"><img src="images/pic02.jpg" alt="" /></span>We had to completely revamp the previous implementation of the full stack and the machine learning algorithms as the basic looking UI/UX was using an outdated Bootstrap v2 and the accuracy was a mere ~50 percent accuracy for diagnosing arrhythmia. We first started off with EKG data as it is much simpler to interpret compared to EEG data. Thus, I researched features that can be interpreted from electrocardiogram (EKG) data to classify heart beats with machine learning and documented my findings. I also parsed the EKG files using Javascript into bitstream and converted the bitstream into JSON formats to be sent over to the server. On the frontend side I also worked on the overall look and functionality of the web app using HTML, CSS, and Bootstrap v4.0. I also got brief exposure to Angular and MongoDB as we prepare for further work over next semester.</p>

								<h4>Other Stuff</h4>
								<p><span class="image left"><img src="images/pic01.jpg" alt="" /></span><p>BlueML is a subteam under the Multidisciplinary Design Program Project "SOCR Big Data & Predictive Analysis" at the University of Michigan led by Professor Ivo Dinov. We coordinated tasks using GitHub with ZenHub and utilized an Agile Development style.</p>

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