Disease prediction by Machine Learning over Big Data from Healthcare Communities

mediumThis post was originally published by Krish Pagar at Medium [AI]

Doctors are working with statisticians and computer scientists to develop better tools for predicting disease. Experts in this field are working on methods for identifying and developing algorithms and models for machine learning that can provide accurate predictions. Is it possible to obtain more accurate results in predicting disease by using big data and integrating data from a variety of sources, such as medical records?

The improvements in predictive accuracy found in the current study explored the potential to use machine learning to predict other disease outcomes. The data collected in this study, conducted in collaboration with the National Institute of Neurological Disorders and Stroke (NINDS) and the US Centers for Disease Control and Prevention (CDC), can be used to develop new algorithms and models for machine learning on other large clinical data sets.

The potential of machine learning to improve medical prognosis and diagnosis is associated with tremendous enthusiasm. With the improvement of the computing capacity of health systems, the use of machine learning to improve disease risk prediction in clinical practice becomes a realistic option. We will need a wide range of machine applications, not only for disease prediction but also for other clinical outcomes.

Several high-profile publications have shown a lack of transparency in the development of ML and AI-based prediction models for disease prediction and diagnosis.

To date, the healthcare industry has not yet recognized the potential benefits of big data analytics. We propose to overcome these problems and improve health predictions by applying deep learning and information fusion to big data systems. By applying deep learning to larger health data, we can learn a hierarchy of traits by establishing high-level traits.

Data analysis and machine learning can be used to design intelligent systems to extract hidden information in the form of medical data and to use expert systems for early detection and treatment. Today, big data, which has been a major focus of recent developments, is increasingly being used for this purpose. Recently, we have attracted the attention of the US Department of Health and the US Food and Drug Administration (FDA).

To promote early detection and disease prevention, we have presented several smart approaches to the US Food and Drug Administration (FDA) and the National Institutes of Health (NIH).

In today’s digital world, various scientists have developed multiple clinical decision support systems to simplify and ensure diagnosis. This paper examines the potential to suggest data mining and machine learning used by various researchers. Big data has been widely used to predict diseases such as heart disease, where different accuracies have been achieved.

But only marginal gains have been achieved, and more complex models, involving multiple geographically diverse data sources, are needed to increase the accuracy of predicting early outbreaks. This approach, developed with Ph.D. student Qinghan Xue, uses a large dataset to demonstrate an improved disease prediction model that combines data cleansing and careful selection of functions with effective machine learning techniques.

In 2012, Prize4Life launched a crowdsourcing competition to develop a method for accurately predicting ALS outcomes based on the PRO-ACT dataset. Chuah used the data set published by Prize 4Life, which helped develop the largest pool of ALS prediction data ever created.

The average number of published papers on AI and machine learning is nearly 25,000, and population-based cohort studies average about 1,500. Although the models are similar in prediction performance to the data, artificial neural networks play an important role in prediction due to their data-driven distribution and are considered as potential modeling approaches in the development of prediction tools. They were compared with a model used to predict ED visits in at-risk cancer patients.

The noise level in this area has taken things to the point where serious people no longer consider a paper of more than 1000 words, or even a few hundred words, useful, and the noise level in this area exceeds the limits of what is considered useful.

The combination of big data and machine learning is a revolutionary technology that, if used properly, can have a major impact on the industry. The heart — disease record is there, and it is a very good — examined data set with a large number of data points. We will use it as a starting point for developing a new type of prediction algorithm for heart disease.

And someday this Technology will be able to detect the future pandemics.

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This post was originally published by Krish Pagar at Medium [AI]

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