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Posted: February 4th, 2024

Diabetes Risk Prediction using Deep Learning and Big Analytics

Diabetes Risk Prediction using Deep Learning and Big Analytics

Diabetes is a chronic disease that affects millions of people worldwide. It occurs when the body cannot produce or use insulin properly, leading to high blood glucose levels. Diabetes can cause serious complications such as heart disease, kidney failure, blindness, and amputation. Therefore, early detection and prevention of diabetes are crucial for improving the quality of life and reducing the health care costs.

One of the challenges in diabetes prediction is to deal with the large and complex medical data that contain various types of information, such as demographic, clinical, laboratory, and textual records. Traditional machine learning techniques may not be able to handle such data effectively, as they require manual feature engineering and selection, which can be time-consuming and prone to errors. Moreover, they may not be able to capture the nonlinear and hidden patterns in the data that are relevant for diabetes prediction.

Deep learning is a branch of machine learning that uses multiple layers of artificial neural networks to learn from data automatically. Deep learning can handle big and heterogeneous data without the need for manual feature engineering and selection. It can also learn complex and abstract features that can improve the prediction performance. Deep learning has been successfully applied to various domains, such as computer vision, natural language processing, and speech recognition.

In this blog post, we will review some of the recent studies that use deep learning techniques for diabetes risk prediction. We will also discuss the advantages and challenges of using deep learning for this task, and provide some suggestions for future research directions.

Deep Learning Techniques for Diabetes Risk Prediction

Several deep learning techniques have been proposed for diabetes risk prediction, such as deep belief networks (DBNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. These techniques can be applied to different types of data, such as structured, unstructured, or mixed data.

Structured data are data that have a predefined format and schema, such as numerical or categorical values. For example, age, gender, blood pressure, blood glucose, and body mass index are structured data that can be used for diabetes prediction. Unstructured data are data that do not have a predefined format or schema, such as text, images, audio, or video. For example, admission and discharge notes, medical reports, and medical images are unstructured data that can contain useful information for diabetes prediction. Mixed data are data that contain both structured and unstructured data.

DBNs are a type of deep learning technique that consist of multiple layers of restricted Boltzmann machines (RBMs). RBMs are stochastic neural networks that can learn the probability distribution of the input data. DBNs can be trained in a layer-wise manner using unsupervised learning, which does not require labeled data. DBNs can be used to extract high-level features from structured or unstructured data, and then feed them to a classifier, such as logistic regression or softmax regression, for diabetes prediction.

For example, Vidhya and Shanmugalakshmi (2020) proposed a DBN-based model for predicting complications of type 2 diabetes mellitus using unstructured textual medical records . They collected 1000 records from diabetic patients with different complications, such as cardiovascular disease, nephropathy, neuropathy


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