مطالب

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.

China has become the world’s high-growth and second largest economy since its socialist- orientation opening-up reforms in the early 1980s. Due also to its geographical, cultural and historical attractions, the country has become the world’s fourth largest tourism destination and tenth largest tourism earner in 2018 according to the World Tourist Organization. The important role played by tourism in the Chinese economy has been officially recognized by China’s National Development and Reform Commission as a strategic pillar industry development priority. In spite of these, rigorous policy-related studies on China’s tourism have been limited. The paper is a serious econometric study to investigate the determination of China’s tourism and its contribution to the country during the period 1996-2018 for credible data-based policy analysis.  Significantly, the study is carried out appropriately from an economic integration (globalization) growth framework, which is also the expenditure (as opposed to production or income) perspective of the United Nations System of National Accounts 1998/2003. Specifically, a multi-simultaneous equation model of endogenous growth and China’s tourism determination is developed. The model innovatively incorporates gravity theory and classical consumer demand contributors, Ironmonger-Lancaster new commodity attributes and Johansen policy impact add- and sub-factors (i.e., reforms and crises) explicitly in its economic integration structure. The model is then estimated by system methods with official economic and tourism 1996-2018 data from the World Tourism Organization and international databases. The research will contribute to advances in the literature and the findings provide useful insights and appropriate and much needed evidence-based inputs on the determination and contributors of tourism to China’s growth. Recommendations will be provided to key stake-holders such as tourism policy-makers, academic researchers, business analysts and tourism operators for national strategic policy analysis and practical implementation. JEL Classification: C54, F15, F62, Z32, Z38