Machine learning is a subset of Artificial Intelligence when combined with Data Mining techniques plays a promising role in the field of prediction. We live in an era where data generation is exponential with time but if the generated data is not put to work or not converted to knowledge data, its generation is of no use. Similarly, in Healthcare also, data availability is high, so is the need to extract the information from it for better prognosis, diagnosis, treatment, drug development, and overall healthcare. In this research, we have tried to focus more on diagnosis of Diabetes disease, which is one of the fastest growing chronic diseases all over the world as declared by World Health Organization in the year 2014. We have also tried to show the different techniques like Gradient Boosting, Logistic Regression and Naive Bayes, which can be used for the diagnosis of diabetes disease with attained accuracy as 86% for the Gradient Boosting, 79% for Logistic Regression and 77% for Naive Bayes.

Cloud computing has emerged as the primary choice for developers in developing applications that require high-performance computing. Virtualization technology has helped in the distribution of resources to multiple users. Increased use of cloud infrastructure has led to the challenge of developing a load balancing mechanism to provide optimized use of resources and better performance. Round robin and least connections load balancing algorithms have been developed to allocate user requests across a cluster of servers in the cloud in a time-bound manner. In this paper, we have applied the round robin and least connections approach of load balancing to HAProxy, virtual machine clusters and web servers. The experimental results are visualized and summarized using Apache Jmeter and a further comparative study of round robin and least connections is also depicted. Experimental setup and results show that the round robin algorithm performs better as compared to the least connections algorithm in all measuring parameters of load balancer in this paper.