Deep convolutional fuzzy neural networks with stork optimization on chronic cardiovascular disease monitoring for pervasive healthcare services.

Cardiovascular disease (CVD) is one of the severe disorders that requires effectual solutions. CVD mainly affects heart functionality in the human body. The impacts of heart disorders are hazardous, which primarily spread from arrhythmia and higher hypertension to heart attack or stroke and also death. Employing newly established data analysis techniques and inspecting a patient's health record might help recognize CVD promptly. In general, pervasive healthcare (PH) services have the potential to enhance healthcare and the excellence of the lifespan of chronic disease patients over constant monitoring. However, the conventional risk evaluation techniques are neither dynamic nor accurate because they stick to the arithmetical data and ignore the significant time-based effects of the crucial signs. So, recent work has utilized machine learning and deep learning methodologies for predicting CVD on clinical datasets. These methods can decrease death rates by predicting CVD depending on the medical data and the patient's severity level. This manuscript presents a deep convolutional fuzzy neural networks with stork optimization on cardiovascular disease classification (DCFNN-SOCVDC) technique for PH services. The main goal of the DCFNN-SOCVDC method is to detect and classify CVD in the healthcare environment. At first, the presented DCFNN-SOCVDC model performs data preprocessing by utilizing Z-score normalization to preprocess the medical data. For the feature selection process, the presented DCFNN-SOCVDC technique utilizes an arithmetic optimization algorithm model. Besides, the deep convolutional fuzzy neural network (DCFNN) method is employed to identify and classify CVD. Eventually, the presented DCFNN-SOCVDC approach employs a stork optimization algorithm method for the hyperparameter tuning method involved in the DCFNN model. The performance of the DCFNN-SOCVDC approach is evaluated using a CVD dataset, and the results are assessed based on various metrics. The performance validation of the DCFNN-SOCVDC approach portrayed a superior accuracy value of 99.05% over recent models.