Ischemic Stroke Post-treatment Prediction using Machine Learning to develop Web Application for Healthcare Centers in Thailand

Stroke attack is one of the most serious concerns in an emergency room. Expert diagnosis and accurate treatment are crucial for the improvement, death, or impairment of the patients. This work aims at using big data analytics for developing a system to provide the probability for each patient whether he would improve after medications. Records of N=1700 volunteers were gathered for non-linear classification training. Random Forest outperforms seven other methods compared in this study. The AI model was deployed as a web application for a rural hospital to assist medical personnel. This can be considered an important step towards AI-assisted emergency medications for stroke. Under the situation where the NIH Stroke Score was unavailable on time, the system could still provide the improvement probability via a web application based on the AI model trained in this work.