Preventing crimes against public health with artificial intelligence and machine learning capabilities

Highlights

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For the safety of the public, it is very important to crack down on criminal acts that endanger public health in accordance with the law.

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This article uses data mining technology to analyze and predict crimes that endanger public health.

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Studies have concluded that the joint assistance of the government and the people can effectively prevent the occurrence of events that endanger public health.

Abstract

Criminal acts that endanger public health have seriously threatened people's health and life. How to prevent such criminal acts from occurring has become the focus of attention from all walks of life. There are few studies on the prevention of crimes endangering public health, and the results are not satisfactory. With the rapid development of artificial intelligence technology, machine learning algorithms are widely used in various fields. Based on the background of the times, this paper applies machine learning algorithms to the prevention research of crimes endangering public health, aiming to improve the efficiency of crime prevention. First of all, this paper establishes a predictive criminal behavior model based on support vector machine and random forest algorithm, and uses the model to analyze its performance. This article takes a certain city in our province as a specific investigation object, collects relevant case data of criminal acts endangering public health in the city from January to October 2018, and predicts criminal behaviors, and compares them with the actual crimes data collected later. At the same time, a random questionnaire survey was conducted on the citizens of this city to analyze the factors leading to crimes that endanger public health and their enthusiasm for participating in legislation. The experimental results show that Lagrangian interpolation can make the data set more complete, with a standard deviation of 1.19; the crime prediction model based on support vector machine and random forest algorithm can basically predict the incidence of crime, and the trend of its predicted data is basically consistent with the trend of actual data; 48.32% of the people believe that imperfect laws and regulations are the main reason for the frequent occurrence of crimes endangering public health, but only 18% are willing to actively participate in relevant legislation. The above results show that the prediction model established by artificial intelligence algorithm can effectively predict criminal behaviors that endanger public health and provide reliable data for prevention.