{"title":"A Novel Analysis of Performance and Inference Time of Machine Learning Models to Detect Cardiovascular Emergency Situations of Rescue Patients","authors":"Abu Shad Ahammed, Micheal Ezekiel, R. Obermaisser","doi":"10.1109/ICAIoT57170.2022.10121844","DOIUrl":"https://doi.org/10.1109/ICAIoT57170.2022.10121844","url":null,"abstract":"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.","PeriodicalId":297735,"journal":{"name":"2022 International Conference on Artificial Intelligence of Things (ICAIoT)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124998358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hadeel Tariq Ibrahim, Wamidh Jalil Mazher, O. Ucan
{"title":"AIoT in Healthcare: A Systematic Mapping Study","authors":"Hadeel Tariq Ibrahim, Wamidh Jalil Mazher, O. Ucan","doi":"10.1109/ICAIoT57170.2022.10121743","DOIUrl":"https://doi.org/10.1109/ICAIoT57170.2022.10121743","url":null,"abstract":"The Internet of Things (IoT) infrastructure and artificial intelligence (AI) technologies are combined to create artificial intelligence of things (AIoT). AIoT in healthcare is a key factor in enabling physicians’ offices and hospitals as well as giving patients access to superior scientific facilities. To this end, we conduct a Systematic Mapping Study (SMS) to provide critical information about various applications of AIoT in healthcare. The primary goals of this research are to provide an overview of AIoT research in healthcare and to categorize AIoT research based on annual number of publications, publication venues, and journal distribution. We present some AIoT techniques used in healthcare fields in nine well-known online libraries (IEEE, Springer, Elsevier, MDPI, Taylor and Francis, Hindawi, Wiley online lib., ACM and Google). Initially, we used a number of research questions related to the issue, and when we applied them, 71 studies were produced.","PeriodicalId":297735,"journal":{"name":"2022 International Conference on Artificial Intelligence of Things (ICAIoT)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114054114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}