Predictive Diagnostics

The inspiration for Predictive Diagnostics originates from high malpractice rates in the ER. This predictor is intended to be used by nurses who can confirm a physician's diagnosis based on factors such as lab test results, demographics, and patient history.

We correctly predicted kidney disease and diabetes cases in a three-class classification problem with 84.6% accuracy using logistic regression in a 10-fold cross-validation. We chose a C value of 0.1 to achieve the balance between the hyperplane overfitting the model even under a lower cost function and overgeneralizing the underlying pattern and thus misclassifying cases.

This predictive tool will decrease healthcare costs and co-pays, patient re-admittance, and defensive medicine practices, while significantly improving the quality of patient care by reducing instances of misdiagnoses and subsequently alleviating the need for specialist appointments. Medhacks 2017

Presentation Slides

https://github.com/tyang27/PredictiveDiagnostics

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