Diabetes mellitus, a chronic disease that has a significant influence on human lives, families, and communities globally, has reached alarming levels and is therefore a leading economic challenge of the twenty-first century. The International Diabetes Federation (IDF) reports that in 2019, 463 million adults aged 20 to 79 globally had diabetes, and predictive analyses estimate that the incidence will rise to 700 million in 2045. This chapter reviews leading studies of diabetes and machine learning techniques for its prevention. The chapter also examines analytical models and technologies for simulating and predicting the incidence of diabetes efficiently. These models help find the disease early to reduce medical costs and prevent more complex health problems, enabling decision-makers to develop solutions to reduce the negative impact and assess the future cost of treatment.

Multicore architecture of CPU is popular because of its performance; the challenge for the Multicore environment are-writing the effective code that can exploit the parallelism, measuring the performance in terms of CPU individual core utilization. The effective code using multithreading (parallel code) leads to performance speedup. Various multithreading applications are getting developed now days to utilize the CPU cores. In this paper, tools are developed, one by using C# console viz. application for measuring the performance of the CPU cores individually. Performance is measured in terms of load on each core in percentage. Second tool is designed using windows C# viz. application for plotting the graph with respect to time of CPU load in percentage. By both the tools performance is measured while quicksort is getting executed in the serial and parallel for a large number of data elements. Experiment is done on dual core and quad core CPU and results are stored in the table. Comparison graphs are drawn for running time of quicksort as well as CPU individual core utilization. The result shows parallel version of quicksort better utilize the CPU individual cores compared to its sequential version. It exploits more parallelism that leads the better CPU utilization.