Smart Medical Prediction for Guidance: A Mechanism Study of Machine Learning.

Xiangming Wang,Baobao Dong

doi:10.1155/2021/2474473

Abstract

Data analysis and prediction have gradually attracted more and more attention in the smart healthcare industry. The smart medical prediction system is of great importance to the enterprise strategy and business development, and it is also of great value to provide medical advices for patients and assist patient guidance. The research theme is the use of machine learning technologies with the application in the areas of smart medical analysis. In this paper, the actual data of the smart medical industry were statistically analysed and visualized according to the features, and the most influential feature combinations were selected for the establishment of the prediction model. Based on machine learning technology, namely, random forest, the guidance prediction model is established, and the combination of features is repeatedly adjusted to improve its accuracy. The practical significance of this paper is to provide a high-precision solution for smart medical data analysis and to realize the proposed data analysis and prediction on the cloud platform based on the Spark environment.

Highlights

With the development of big data, data analysis and prediction have gradually attracted attention in various industries, and the application of data has become more extensive
E RMSPE value was reduced from 0.50009 to 0.47092, and the error value was reduced by 5.8%. en, we analysed the influence of each characteristic value on the prediction model and removed the less influential features one by one, such as Open and National holiday. e scores of MAPE and RMSPE in the prediction model were both improved
We selected Hospital, Day of week, Sales, School holiday, Month, Year, and other features to establish the prediction model, and the MAPE value of random forest decreased from 0.31150 to 0.27398. e error value decreased by 12.0%, RMSPE value decreased from 0.50009 to 0.40755, and error value decreased by 18.5%

Summary

Introduction

With the development of big data, data analysis and prediction have gradually attracted attention in various industries, and the application of data has become more extensive. Whether in e-commerce, financial transactions, healthcare, or marketing, the use of big data has become a trend. In the era of intelligent medical treatment with the explosive growth of network information, the amount of data to be processed has become larger, the speed of data generation and processing has become faster, the data sources have become more diversified, and the analysis technology of big data has become more complex, flexible, and powerful. Erefore, the application of Spark in the smart medical industry has become a wildfire, especially for the analysis and prediction of guidance data [3, 4] Spark can operate at a much faster speed than Hadoop, and its application scope is wider. erefore, the application of Spark in the smart medical industry has become a wildfire, especially for the analysis and prediction of guidance data [3, 4]

Objectives

Results

Conclusion