Abstract
In recent years, with the increase of computer computing power, Deep Learning has begun to be favored. Its learning of non-linear feature combinations has played a role that traditional machine learning cannot reach in almost every field. The application of Deep Learning has also driven the advancement of Factorization Machine (FM) in the field of recommendation systems, because Deep Learning and FM can learn high-order and low-order features combinations respectively, and FM's hidden vector system enables it to learn information from sparse data. The integration of them has attracted the attention of many scholars. They have researched many classic models such as Factorization-supported Neural Network (FNN), Product-based Neural Networks (PNN), Inner PNN (IPNN), Wide&Deep, Deep&Cross, DeepFM, etc. for the Click-Through-Rate (CTR) problem, and their performance is getting better and better. This kind of model is also suitable for agriculture, meteorology, disease prediction and other fields due to the above advantages. Based on the DeepFM model, we predicts the incidence of hepatitis in each sample in the structured disease prediction data of the 2020 Artificial Intelligence Challenge Preliminary Competition, and make minor improvements and parameter adjustments to DeepFM. Compared with other models, the improved DeepFM has excellent performance in AUC. This research can be applied to electronic medical records to reduce the workload of doctors and make doctors focus on the samples with higher predicted incidence rates. For some changing data, such as blood pressure, height, weight, cholesterol, etc., we can introduce the Internet of Medical Things (IoMT). IoMT's sensors can be used to conduct transmission to ensure that the disease can be predicted in time, just in case. After joining IoMT, a healthcare system is formed, which is superior in forecasting and time performance.
Highlights
In the 1950s and 1960s, neural networks were proposed
The internet of things (IoT) solution we propose is as follows: wearable sensors such as bracelets will regularly collect user weight, blood pressure, cholesterol and other changing data, and transmit it to smart-phones through a lightweight protocol Constrained Application Protocol (CoAP) [45]
In this article, we use the Factorization Machine (FM)-based neural network model that was originally applied to the CTR problem, DeepFM, to predict the presence or absence of hepatitis in this data set sample based on the structured data for disease prediction in the preliminaries of the Smart Calculation 2020 Artificial Intelligence Challenge
Summary
In the 1950s and 1960s, neural networks were proposed. Inspired by human brain neurons, this model consists of multiple neurons that receive and process signals from connected neurons/nodes. Sumit Sharma et al have proposed Recurrent Neural Network (RNN)-based Alzheimer’s prediction scheme that uses trigger-based sensors to collect sensorimotor data in the Internet of Health (IoH) [57], [58] ecosystem, which is nearly 10-20% more accurate than existing machine learning algorithms [22]. Hossain proposed a cloud-based patent and health monitoring model in the cyber physical environment, which has high efficiency and accuracy [23]; Deep Convolutional Neural Network (DCNN) and IoT are used in oral cancer image classification. It can achieve 96.8% accuracy and 92% sensitivity [24]. It is feasible to apply Deep Learning and the IoT to medicine
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