Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2863 2019 2019 Assistant professor-senior scale Department of Information and Communication Technology. Manipal Institute of Technology, Bengaluru Madhura Madhura Associate Professor - 1, Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, Karnataka, India Asha KS Assistant professor Ece Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi, Chennai, Tamil Nadu Mary Christeena Thomas Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India Anubhav Bhalla Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India Rajat Saini College of Medical Techniques, Al-Farahidi University, Baghdad, Iraq Aws Zuhair Sameen Reducing the influence of significant noise signal components on the obtained raw ECG signal is essential for precise identification of cardiac arrhythmias (CA), which frequently present as irregularities in heart rate or rhythm. Preprocessing is used to remove noise signals and baseline drift from the ECG wave that is recorded using the internet of things (IoT). After that, the denoised signal is subjected to dimensionality reduction and feature extraction. In order to determine whether classification method is more effective in detecting cardiac arrhythmias, this study compares two methods: an adaptive neuro-fuzzy inference system and artificial feed-forward neural networks trained with the back-propagation learning algorithm. An Adaptive Neuro Fuzzy Inference System analyses ICA features obtained by non-parametric power spectral estimates, and an Artificial Neural Network (ANN) classifier uses the ECG signal's morphological and statistical aspects to identify patterns. The creation of artificial feed-forward neural networks provides a rich framework for studying the Back Propagation Algorithm. Sensitivity, specificity, accuracy, and positive predictiveivity are some of the performance characteristics that are thoroughly examined. An overall accuracy of 97.79%, sensitivity of 99.82%, specificity of 99.68%, and positive predictivity of 98.58% were seen in the results of the Artificial Neural Feed Forward Network (ANFFN). The Adaptive Neuro Fuzzy Inference System (ANFIS) outperforms these metrics with an astounding overall accuracy of 99.62%, specificity of 98.63%, and positive predictivity of 99.46%. With a classification accuracy of 99.82%, ANFIS demonstrates to be the most effective classifier for identifying cardiac arrhythmias. 2024 2024 99 110 10.54216/JISIoT.130108 https://www.americaspg.com/articleinfo/18/show/2863