The effects of 5G network on people and the environment: A machine learning approach to the comprehensive analysis

Abstract

The progression of telecommunications, starting from the inception of 1G networks in 1979 to the advent of 5G technology in 2019, represents a significant journey of advancement for humanity. As we approach the era of 5G, characterized by heightened machine-to-machine connectivity and transformative applications in AI, IoT, and cloud computing, it becomes imperative to acknowledge and address concerns regarding its potential impacts on health and the environment. Utilizing machine learning algorithms, particularly implemented in Python for this research, provides a potent approach to analyzing intricate datasets concerning 5G signals and their potential correlations with healthcare outcomes. After carefully cleaning and preparing the data and conducting linear regression analysis, uncovered evidence backing the notion that 5G antennas emit greater levels of radiation compared to 4G antennas emerged - a fact often concealed by corporations. Despite relying on a restricted dataset, the results emphasize the necessity for more accurate data to improve model precision. Ongoing research endeavors are vital to alleviate public anxieties regarding 5G technology, thereby fostering trust and bolstering awareness on a wider front.