To Detect Early Sepsis Disease using Clinical Data
Sepsis is activated by the immune system present in your body that works all the time in order to prevent the infection from entering. During this stage, the enormous number of synthetic substances discharged into the blood causes broad irritation. For the patient the practicality of detecting sepsis disease occurrence in development is an important factor in the result. The primary goal of this work is to build ,train and test a Multi Layer Perceptron (MLP) model using data that is available in the form of electronic clinical health data and predicts outcome of class labels as sepsis or no-sepsis for unseen health records. The secondary goal is to compare the accuracy of the MLP against many other models like Ada-Boost, Gradient Boosting, etc using the metrics accuracy and log loss.
Deployed the code At: https://sepsis-detection.herokuapp.com/
DataSet Available At: https://physionet.org/content/challenge-2019/1.0.0/