The proposed ABC-ANFIS model achieved 93% accuracy for classifying the murmur class in heartbeat sounds, outperforming other existing models.
Does an optimized ABC-ANFIS model improve the accuracy of heartbeat sound classification compared to existing machine learning models?
A novel ABC-ANFIS machine learning model achieved 93% accuracy in classifying heartbeat murmurs, demonstrating potential to assist physicians in early cardiovascular disease detection.
Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.
Keikhosrokiani et al. (Sun,) conducted a other in Cardiovascular disease (heartbeat sound murmurs). Adaptive Neuro-Fuzzy Inferences System (ANFIS) optimized by artificial bee colony (ABC) vs. ANFIS, PSO ANFIS, SVM, KSTM, KNN was evaluated on Accuracy for the murmur class. The proposed ABC-ANFIS model achieved 93% accuracy for classifying the murmur class in heartbeat sounds, outperforming other existing models.