ETL-FEXIC Model for Secured Heart Rate Abnormality Healthcare Framework

Abstract

In traditional methods, it is critical for an effective continuous pulse monitor for humans prone to heart rate abnormalities. This paper proposes a secured heartrate abnormality detector which continuously monitors human pulse rate and SpO2 level. The current studies proposes that machine learning (ML) models performs well in classification; also, TinyML model shows better performance for data from resource constrained IoT devices. Hence, the research first analyses abnormal heart rate detection and spam data identification using standard ML algorithms such as SVM, Random Forest, Decision Tree, and TinyML. Though ML models are superior in classification, deep learning approaches outperforms them in feature learning. Hence, our proposed framework combines the merits of both ML and DL models. In our approach, the generated healthcare dataset is fed to DL models such as ANN, and autoencoder and also to SHAP XAI (eXplainable Artificial Intelligence) for feature extraction and learning. These learnt features are fed to ML models for classification. In this experiment, the proposed ETL-FEXIC (Enhanced Tiny Machine Learning with Automated Feature Extraction) outperforms the other ML models where the extracted features from XAI is fed to optimized TinyML classification model.