An In-Depth Evaluation of Machine Learning Techniques for Anticipating Effective Human Health Outcomes

Healthcare sector is a very promising sector that is becoming very important these days with variant advancements. Such an emergence has lead to effective outcomes towards human health. This paper focus on maintaining an effective human health outcome with the use of machine learning approaches. This study uses in-depth evaluation of Linear regression, Polynomial regression, and XG Boost approaches. Such a study is very useful for facilitating the resource distribution and an efficient service delivery depending upon sales. This work uses computation of sales to predict community healthcare needs. Thus, this strategy improves patient happiness and medical results.