Iranian Journal of

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Introduction: Aspects of social and economic development in ICT has led to new applications of these technologies. Using the portal geriatrics’ is an access point to a wide range of resources and information in the content for geriatric medicine specialists, physicians and other health care workers, elderly and their families. It provides integrated information on the sources and applications of heterogeneous. The National Portal of Geriatric Medicine is the best solution to resolve this problem.This study compares the experience of few developed countries and offer the geriatrics’ portal. Methods: This paper is based on valid studies, library and internet searches in databases like as science directly, Springer, Proquest, and advanced search in Google to review the literature on the geriatrics portal in selected the countries. Findings: Developing a portal is a strategy to support the development and maintenance of all the desirable features of the portal and user needs’ analysis. It could also, characterize the structure and integrated system of geriatric care. It makes the integrity to fulfill of the main condition portal services and the content that offer to the elderly.

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Background: Diabetes entails a great quantity of deaths each year and a great quantity of people living with the disease do not find out their health status early sufficient. In this paper, we advance a data mining-based model for prematurely diagnosis and prediction of diabetes.
Methods: Although K-means is simple and can be utilized for a vast diversity of data kinds, it is wholly sensitive to initial locations of cluster centers which specify the final cluster result, which either enables an efficiently and adequate clustered dataset for the logistic regression model, or presents a lesser amount of data as a result of wrong clustering of the main dataset, thereby restricting the proficiency of the logistic regression model. The main purpose of this study is was to specify procedures of ameliorating the k-means clustering and logistic regression accuracy consequence. Therefore, our algorithm comprises of principal component analysis technique, k-means technique and logistic regression model.
Results: The results obtained from this study show that the ability to obtain the result of K-means clustering accuracy is much higher than what other researchers have obtained in similar studies. Also, compared to the results obtained from other algorithms, the logistic regression model was implemented at an improved level in predicting the onset of diabetes. Another real advantage is that the proposed algorithm was able to successfully model a new dataset.
Conclusion: In general, the proposed approach can be effectively used in predicting and early diagnosis of diabetes.