Importance of Watermark Lossless Compression in Digital Medical Image Watermarking

Large size data requires more storage space, communication time, communication bandwidth and degrades host image quality when it is embedded into it as watermark. Lossless compression reduces data size better than lossless one but with permanent loss of important part of data. Data lossless compression reduces data size contrast to lossy one without any data loss. Medical image data is very sensitive and needs lossless compression otherwise it will result in erroneous input for the health recovery process. This paper focuses on Ultrasound medical image region of interest(ROI) lossless compression as watermark using different techniques; PNG, GIF, JPG, JPEG2000 and Lempel Ziv Welsh (LZW). LZW technique was found 86% better than other tabulated techniques. Compression ratio and more bytes reduction were the parameters considered for the selection of better compression technique. In this work LZW has been used successfully for watermark lossless compression to watermark medical images in teleradiology to ensure less payload encapsulation into images to preserve their perceptual and diagnostic qualities unchanged. On the other side in teleradiology the extracted lossless decompressed watermarks ensure the images authentication and their lossless recoveries in case of any tamper occurrences

Statistical modeling for prediction of diabetes in Malaysians

Type II Diabetes Mellitus is one of the silent killer diseases worldwide. According to the World Health Organization, 347 million people are suffering from diabetes throughout the world. To overcome the sharp rise in the disease, various diagnostic or prediction models were developed through various techniques such as artificial intelligence, classification and clustering, pattern recognition and statistical methods. The study led to the related open issues of identifying the need of a relation between the major factors that lead to the development of diabetes. This is possible by investigating the links found between the independent and dependant variables in the dataset. This paper investigates the effect of binary logistic regression applied on a dataset. The results show that the most effective method was the enter method which gave a prediction accuracy of almost 93%

The Long-Term Prediction of Type II Diabetes Mellitus: A Review Study

Type II Diabetes Mellitus is one of the silent killer diseases worldwide. According to the World Health Organization, 346 million people are suffering from diabetes worldwide. In this paper, we reviewed some models and systems used for diabetes prediction, comparing and exploring various related research works. We found some models and systems to predict and diagnose type II diabetes mainly for a short term. This led to the related open issues of the development of a model for the prediction of type II diabetes in the long run. We analysed the need of a relation between the major factors that lead to the development of diabetes. We aim to study the pattern behaviour in existing data. Furthermore, we have proposed to classify the data using k-means algorithm and the prediction through particle filter method. The model would be able to do long term prediction for potential persons. This would be beneficial to overcome the sharp rise of diabetes globally

A comparative study on the pre-processing and mining of Pima Indian diabetes dataset

Data mining in medical data has successfully converted raw data into useful information. This information helps the medical experts in improving the diagnosis and treatment of diseases. In this paper, we review studied data mining applications applied exclusively on an open source diabetes dataset. Type II Diabetes Mellitus is one of the silent killer diseases worldwide. According to the World Health Organization, 346 million people are suffering from diabetes worldwide. Diagnosis or prediction of diabetes is done through various data mining techniques such as association, classification, clustering and pattern recognition. The study led to the related open issues of identifying the need of a relation between the major factors that lead to the development of diabetes. This is possible by mining patterns found between the independent and dependant variables in the dataset. This paper compares the classification accuracies of non-processed and pre-processed data. The results clearly show that the pre-processed data gives better classification accuracy

Infant & Child Mortality in Pakistan and its Determinants: A Review

Over the years, several developing countries have been suffering from high infant and child mortality rates, however, according to the recent statistics, Pakistan falls high on the list. Our narrative review of copious research on this topic highlights that several factors, such as complications associated with premature births, high prevalence of birth defects, lack of vaccination, unsafe deliveries, poor breastfeeding practices, complications during delivery, sudden infant death syndrome (SIDS), poor socioeconomic conditions, and a struggling healthcare system, have influenced these rates. Bearing in mind the urgency of addressing the increased infant and child mortality rate in Pakistan, multiple steps must be taken in order to prevent unnecessary deaths. An effective initiative could be spreading awareness and education among women, as a lack of education among women has been indirectly linked to increased child mortality in Pakistan across many researches conducted on the issue. Furthermore, the government should invest in healthcare by hiring more physicians and providing better supplies and improving infrastructure, especially in underdeveloped areas, to decrease child mortality due to lack of clean water and poor hygiene. Lastly, telemedicine should be made common in order to provide easy access to women who cannot visit the hospital