. Congenital heart failure (CHF) due to congestion in the blood is a serious cardiac problem correlated with crippling symptoms and leading to a rising death rate, monumental health care spending, and reduced quality of life. Heart disease prevention is among the most crucial functions of any medical system, as many people are prone to heart attacks worldwide. Although several segmentation methods for great vessels and the heart have been proposed in the research, they are not successful when applied to the health records of congenital heart disease. In this proposed work, the thickness and fat accumulation of most arteries are measured and analyzed, and then the measurement is synthesized with the corresponding width of the blood vessels of arteries; this data is used for training purposes in the convolutional neural network with one-off cross-validation and regularization. Using the CNN model, a confusion matrix is created and different statistical parameters such as accuracy sensitivity, specificity, precision, and f-score are generated. The final average accuracy was 97%, precision was 98.13%, and F-score was 98.36%. The results indicate that the CNN-based strategy can distinguish healthy hearts from those with prior cardiovascular disease.

Data mining, an excellent development technology for discovering and gathering essential knowledge from vast data collection that can help analyze and draw up trends for decision-making in the industry. Talking about the medical sphere, data mining can be used to uncover and withdraw useful data and trends that can be helpful in clinical diagnostic results. The research focuses on the diagnosis of heart disease, taking past evidence and information into account. To achieve this SHDP, non-linear SVC with RBF kernel algorithms is designed to perfect this SHDP (Smart Heart Disease Prediction). The final is a useful algorithm to look for the right combination of hyper parameters to increase the precision of the algorithm (C, α). The requisite data was arranged in a structured way. The following features are derived from medical profiles for the estimation of the risks of heart failure in a patient: BP, age, sex, cholesterol, blood sugar, etc. The collected characteristics serve as an input to the Navies Bayesian heart disease prediction classification. The data collection used is divided into two parts, 80% of the data are used for preparation, and 20% are used for research. The method suggested includes data collection, user authentication, and log in (based on application), classification through Navies Bayesian, prediction, and safe data transmission via the AES application (Advanced Encryption Standard). An average accuracy, specificity, sensitivity, precision, 93.53% f-score, 89.22%, 91.24% and 86.98%, respectively. This method is also possible in clinical settings to help clinicians predict cardiac arrest.

Heart disease is a serious terminal condition in most parts of the world. The acute lack of medical professionals, expertise, and technology to identify important signs. So a smart and efficient model and technology is required to lead early diagnosis of heart disease. The current study proposes a new experience-based method namely HSPUCD (Heart stage prediction using clinical data) to forecast cardiac disorders utilizing hybrid machine learning. Type 4 protocols Cross Validation is used to assess a model's competence on unknown data. Hyper parameters were modified to regulate the suggested model's behavior and performance. The suggested system is based on extensive research. Data pre-processing was utilized to discover the most relevant network traffic and generate the initial candidate collection of feature characteristics. The suggested model additionally uses automated feature selection to extract features from a candidate set of features and selects the most relevant subset of features from this candidate set. Crow search algorithm is used to extract data characteristics from the pre-processed data. Finally, an online training classifier is utilized to categorize the data and forecast the result. The proposed strategy greatly increases the system's illness prediction accuracy with less noise and more advanced regression approaches using existing medical datasets. Mathematical results depicts the effectiveness of the proposed approach in comparison with others.

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