machine learning techniques

Heart disease pretends great danger to people, as heart disease has recently become a dangerous disease that acts as a threat to humans. It usually affects all groups from young to old. The biggest challenge in this paper is data pre-processing and discovering a solution to the failure of records Clinical heart, where an effective high-performance model is proposed to enhance heart disease and treat failure in the clinical heart failure records. The current authors applied the techniques of clustering with k-means, expectation-maximization clustering, DBSCAN, support vector clustering, and random clustering herein. Using cluster techniques, we gained good enough results for significantly predicting and improving the performance of heart disease. The goal of the model is a suggestion of a reduction method to find features of heart disease by applying several techniques. Our most important results are to predict faster and better. It indicates that the proposed model is excellent and gives excellent results. This model demonstrated a great superiority over its counterparts through the results obtained in this research. We obtained some values of 130, 980, 183, 125.133, 133, 203, and 125.800. It confirms that this model will predict significantly and improve the performance of the data that we have worked on this.

COVID-19 is a new variant of SARS-COV-2 which can lead to mild to severe infection in humans. Despite the remarkable efforts to contain the epidemic, the virus spread rapidly around the world and its prevalence continued with different degrees of clinical symptoms in many countries. Although common strategies including prevention, diagnosis, and care are necessary to curb this epidemic, early and accurate diagnosis can play an important role in reducing the speed of the epidemic. In this regard, the use of technologies based on artificial intelligence can be of great help. For this reason, since the outbreak of COVID-19, many researchers have tried to use machine learning techniques as a subset of artificial intelligence for the early diagnosis of COVID-19. Considering the importance and role of using clinical and laboratory data in the diagnosis of people with covid-19, in this paper K-NN, SVM, decision tree, random forest, Naive Bayes, neural network and XGBoost models are the most common machine learning models, and a dataset containing 1354 records consisting of clinical and laboratory data of patients in Imam Hossein Hospital in Tehran has been used to diagnose patients with covid-19. The results of this research indicate that based on the evaluation criteria, XGBoost and K-NN models have the most accuracy among the mentioned models and can be considered suitable predictive models for the diagnosis of COVID-19.