Detection of acute respiratory distress syndrome using support vector machine model in machine learning

When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the correct labels of some patients may adversely affect the performance of the algorithm. For example, even clinical experts may have less confidence when assigning a medical diagnosis to some patients because of ambiguity in the patient’s case or imperfect reliability of the diagnostic criteria. As a result, some cases used in algorithm training may be mis-labeled, adversely affecting the algorithm’s performance.We present Support Vector Machine model along with classification algorithm like random forest,naive bayes and decision tree algorithms for to increase the accuracy level of syndrome detection. We apply supervised learning algorithms. We can able to improve the performance of SVM algorithm to detect the patient with ARDS on hold-on or hold-out and we finalize the accuracy level.