Ischemic Stroke Post-treatment Prediction using Machine Learning to develop Web Application for Healthcare Centers in Thailand
- Resource Type
- Conference
- Authors
- Worraviseat, Kulwarin; Danvirutai, Pobporn; Anutrakulchai, Sirirat; Kasemsap, Narongrit; Srichan, Chavis
- Source
- 2023 International Electrical Engineering Congress (iEECON) Electrical Engineering Congress (iEECON), 2023 International. :54-57 Mar, 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Training
Support vector machines
Machine learning algorithms
Hospitals
Stroke (medical condition)
Predictive models
Prediction algorithms
stroke
artificial intelligence
AI-assisted diagnosis
precision medicine
random forest algorithm
web application
- Language
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.