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Crowdsourcing from Data Science Bowl Result in Heart Disease Diagnosis Algorithm

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dsbby Angela Guess

A new article out of the companies reports, “With the close of the second annual Data Science Bowl, Booz Allen Hamilton and Kaggle proved yet again that crowdsourced solutions can be used to solve some of our world’s most complex problems. This year, the focus was on heart health – an issue of critical importance worldwide. In the US alone, one person is diagnosed with heart disease every 43 seconds. Imagine being the person – the one waiting for the doctor to return with an MRI and your results. It’s a process that typically takes 20 minutes. Now, imagine sitting next to your doctor and receiving your results and prognosis in real-time. That’s what this year’s Data Science Bowl accomplished and why it was so incredibly successful.”

The article continues, “The winners of the Data Science Bowl – people from all walks of life and careers – were challenged to automate the process of analyzing MRI images. The National Heart, Lung, and Blood Institute (NHLBI) provided the data for the competition, more than 1,000 MRI images from a broad sample set, including individuals of different ages and genders. Despite the diversity and complexity of the sample set, more than 993 participants responded to the challenge. Working individually and in teams, more than 1,390 algorithms were submitted for consideration. The winning algorithm was built by Qi Liu and Tencia Lee; hedge fund analysts and self-described quants who has no medical experience and yet were able to create an algorithm that had an accuracy rate on par with the human eye.”

Read more here.

Photo credit: Data Science Bowl

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