ChronicNet: Randome Forest Classifier-based Chronic Heart Failure Detection with CNN Feature Analysis

Chronic heart failure (CHF) is a common illness that affects the heart. The conventional machine learning and deep learning algorithms failed to identify CHF in the early stages. So, this work implemented the ChronicNet by combining the properties of both deep learning and machine learning. This study is all about creating a network for finding out CHF from sound recordings of the heart. These are called phonocardiogram data, or PCG. The first step, which is noise removal, signal boosting, and dividing parts, makes sure that heartbeat sounds are of good quality. We use Mel and pitchbased coefficients (MFCC) to analyze the frequency changes in a heartbeat signal. This helps pick out features that show what makes CHF heart disease more special. The convolutional neural network (CNN) feature extraction, using deep learning, helps the MFCC automatically find out special features on its own. The Random Forest classifier (RFC) is used to build a model that can predict CHF faster. In groups with problems to classify, the RFC uses a set of decision trees. This gives benefits such as holding up, fitting to big sizes, and being highly accurate. The proposed system uses CNN features to find CHF from PCG data and correctly identify it using RFC. The simulation results show that the proposed ChronicNet outperformed traditional approaches with 98.84% accuracy.