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This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care.

This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation.

The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector.

The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.

Predicting the final status of an ongoing process or a subsequent activity in a process is an important aspect of process management. Semi-structured business processes cannot be predicted by precise and mathematical methods. Therefore, artificial intelligence is one of the successful methods. This study aims to propose a method that is a combination of deep learning methods, in particular, the recurrent neural network and Markov chain.

The proposed method applies the BestFirst algorithm for the search section and the Cfssubseteval algorithm for the feature comparison section. This study focuses on the prediction systems of social insurance and tries to present a method that is less costly in providing real-world results based on the past history of an event.

The proposed method is simulated with real data obtained from Iranian Social Security Organization, and the results demonstrate that using the proposed method increases the memory utilization slightly more than the Markov method; however, the CPU usage time has dramatically decreased in comparison with the Markov method and the recurrent neural network and has, therefore, significantly increased the accuracy and efficiency.

This research tries to provide an approach capable of producing the findings closer to the real world with fewer time and processing overheads, given the previous records of an event and the prediction systems of social insurance.