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Diabetes is one of the most common health problems in the world. Diabetes is also known as “the silent killer” because according to WHO (2016) diabetes increased from 108 million in 1980 to about 422 million adults had diabetes in 2014. If not handled properly, diabetes can become chronic and damage other organs and can cause death. This disease has several symptoms in the patient but evaluating the different factors or symptom variables required to determine which variables are more dominant. This research aims to establish the most influential variable of the many variables causing diabetes mess. We suggest using a data mining decision tree (C4.5) in this paper to forecast diabetes to help doctors analyse the disease sooner. Data mining has carried out various approaches to predict a disease, one of them is the use of c4.5. In this research, produce a decision tree and the result shown that polydipsia play a role in diabetes with accuracy 90.38 %. One of the most dominant signs of diabetics is the sign of polydipsia.

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We present an objective reduced-reference image quality assessment method based on harmonic gain/loss information through a discriminative analysis of local harmonic strength (LHS). The LHS is computed from the gradient of images, and its value represents a relative degree of the appearance of blockiness on images when it is related to energy gain within an image. Furthermore, comparison between local harmonic strength values from an original, distortion-free image and a degraded, processed, or compressed version of the image shows that the LHS can also be used to indicate other types of degradations, such as blurriness that corresponds with energy loss. Our simulations show that we can develop a single metric based on this gain/loss information and use it to rate the quality of images encoded by various encoders such as DCT-based JPEG, wavelet-based JPEG 2000, or various processed images. We show that our method can overcome some limitations of the traditional PSNR.

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