A Novel Analysis of Performance and Inference Time of Machine Learning Models to Detect Cardiovascular Emergency Situations of Rescue Patients

Cardiovascular complications are considered as one of the most common and fatal complications to the rescue personnel and require urgent medical intervention to save an emergency patient. Often due to the delay in detecting heart complication in urgent situation, necessary treatment paths cannot be followed which results in high mortality rate. Although the current researches show promising aspects in detecting cardiovascular diseases in early stage, they are focused on detecting only some of specific and common cardiovascular diseases from clinically recorded or long term historical patients’ data. The novel approach followed in this research is: instead of using traditional health data collected in clinical environment, we developed the model with 9 years of rescue mission’s real-time recorded data to recognise any cardiovascular situation in general. To find out the best model, different machine learning(ML) algorithms like Support Vector Machine(SVM), Random Forest(RF), K-nearest neighbour(KNN), Extreme Gradient Boosting(XGB), Logistic Regression(LR), Naive Bayes(NB) and Artificial Neural Network(ANN) were used. From the performance comparison, we concluded that extreme gradient boosting and neural network showed the best performance in terms of all evaluation parameters. Fast inference is the basic requirement for any rescue mission. So an inference time analysis of the ML models and Apache-TVM machine learning compiler was shown to understand their compatibility in real world applications.