Conv-Random Forest-Based IoT: A Deep Learning Model Based on CNN and Random Forest for Classification and Analysis of Valvular Heart Diseases

Cardiovascular diseases are growing rapidly in this world. Around 70% of the world’s population is suffering from the same. The entire research work is grouped into the classification and analysis of heart sound. We defined a new squeeze network-based deep learning model—convolutional random forest (RF) for real-time valvular heart sound classification and analysis using industrial Raspberry Pi 4B. The proposed electronic stethoscope is Internet enabled using ESP32, and Raspberry Pi. The said Internet of Things (IoT)-based model is also low cost, portable, and can be reachable to distant remote places where doctors are not available. As far as the classification part is concerned, the multiclass classification is done for seven types of valvular heart sounds. The RF classifier scored a good accuracy among other ensemble methods in small training set data. The CNN-based squeeze net model achieved a decent accuracy of 98.65% after its hyperparameters were optimized for heart sound analysis. The proposed IoT-based model overcomes the drawbacks faced individually in both squeeze network and RF. CNN-based squeeze net model and RF classifier combined together improved the performance of classification accuracy. The squeeze net model plays a pivotal part in the feature extraction of heart sound, and an RF classifier acts as a classifier in the class prediction layer for predicting class labels. Experimental results on several datasets like the Kaggle dataset, the Physio net challenge, and the Pascal Challenge showed that the Conv-RF model works the best. The proposed IoT-based Conv-RF model is also applied on the selected subjects with different age groups and genders having a history of heart diseases. The Conv-RF method scored an accuracy of 99.37 ± 0.05% on the different test datasets with a sensitivity of 99.5 ± 0.12% and specificity of 98.9 ± 0.03%. The proposed model is also examined with the current state-of-the-art models in terms of accuracy.