Predicting Diabetes Using a Machine Learning Approach - DZone Big Data

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Diabetes is one of deadliest diseases in the world. It is not only a disease but also a creator of different kinds of diseases like heart attack, blindness, kidney diseases, etc. The normal identifying process is that patients need to visit a diagnostic center, consult their doctor, and sit tight for a day or more to get their reports. Moreover, every time they want to get their diagnosis report, they have to waste their money in vain. But with the rise of Machine Learning approaches we have the ability to find a solution to this issue, we have developed a system using data mining which has the ability to predict whether the patient has diabetes or not.


Clinical Utility of Machine-Learning Approaches in Schizophrenia: Improving Diagnostic Confidence for Translational Neuroimaging

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Machine-learning approaches are becoming commonplace in the neuroimaging literature as potential diagnostic and prognostic tools for the study of clinical populations. However, very few studies provide clinically informative measures to aid in decision-making and resource allocation. Head-to-head comparison of neuroimaging-based multivariate classifiers is an essential first step to promote translation of these tools to clinical practice. We systematically evaluated the classifier performance using back-to-back structural MRI in two field strengths (3- and 7-T) to discriminate patients with schizophrenia (n 19) from healthy controls (n 20). Gray matter (GM) and white matter images were used as inputs into a support vector machine to classify patients and control subjects.


Medical Minecraft uses IBM Watson to teach students about infectious diseases

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IBM's Watson is still in its early days, but the cognitive computing system could end up having a substantial impact on a number of industries, particularly healthcare and education. For example, Alder Hey Children's Hospital in England is currently using the technology to improve the patient experience, while an interactive toy called the Cognitoys Dino uses Watson to answer a child's questions in a kid-friendly and personalized way. Another space that could largely benefit from Watson's capabilities is the gaming industry. The interactive nature of games paired with Watson's natural language processing capabilities and data analysis has already led to a number of new gaming initiatives, including the first-ever Minecraft game that utilizes Watson. Called'Medical Minecraft,' the game was recently created by a group of high school students.


Contribute to Women's Health Outcomes Via New Data Science Competition

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According to a World Health Organization (WHO) report released in 2011, about 820,000 women and men aged 15-24 were newly infected with HIV in developing countries. Over 60% of these were women. Among so many other challenges, developing countries are plagued with serious reproductive health illnesses such as sexually transmitted infections (STIs), unintended pregnancies, and complications from childbirth. A key priority for policymakers, researchers, and health care providers working in developing nations is to emphasize prevention and distribution of information about STIs and other reproductive tract infections (RTIs). This report on Improving Reproductive Health in Developing Countries from the U.S. National Academy of Sciences contains additional information on the topic.


IDG Connect UK: A Big Data & Machine Learning Approach to Diabetes?

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Outcomes Based Healthcare and Big Data Partnership have won an Innovate UK grant for a 1m project to change healthcare's approach to diabetes. In partnership, these two organisations will be creating "a dashboard and software product for doctors to predict and pre-treat for complications of diabetes." The press release explains that this will utilise health and non-health data in conjunction with advanced Machine Learning and analytics techniques to develop a system that can identify the progression of the disease. This will come from a local London population. The reason this Big Data and Machine Learning approach is interesting though, is it offers a top-line independent perspective on a difficult disease.