According to American construction safety statistics, about 20% of worker deaths in the United States occur in the construction industry, but construction workers only account for 6% of the American workforce. For the fatal construction industry statistics, one-fifth of the deaths of American workers occurred in the construction industry. As for the Non-Fatal Construction Injuries, The construction industry accounts for 6% of all injuries that result in lost days of work. As a result, it is estimated that fatal construction injuries cost the United States $ 5 billion every year in terms of medical care, lost income, reduced quality of life for family members and lost production. The total cost of workplace injury exceeds $ 170 billion per year.[NSC] Workers' compensation claims for nonfatal falls account for $2.5 billion annually.

Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization in predicting incident outcome in occupational safety is relatively new. This study tests the performance of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible information of injured workers in agribusiness industries using workers’ compensation claims. More than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised machine learning algorithms for classification (support vector machines with linear, quadratic, and RBF kernels, Boosted Trees, and Naïve Bayes) were applied.

The technology can provide key improvements in workers' comp because it can detect new patterns out of things like natural text and images. Why should you care about machine learning (ML)? ML essentially refers to the phenomenon of computers and other devices that appear to be intelligent because they can learn from patterns in the environment to achieve a specified goal or predict an outcome. These patterns could come from a variety of sources, such as images, voice, free text or even structured data. The machine's ability to anticipate or interpret the information given can make it seem almost human-like.