The role of implementing Machine Learning approaches in enhancing the effectiveness of HealthCare service

“Machine Learning” (ML) is a useful technology for extracting information from “Internet of Things” (IoT) data. These hybrid systems intelligently improve decision-making in a variety of fields, including education, business, healthcare as well as security. IoT can use machine learning to decipher hidden patterns in large amounts of data, allowing for better forecasting and recommendation engines. In healthcare, ML is utilised for disease assessment and treatment predictions, as well as target discovery and verification, pharmaceutical product development, biomarker recognition, and pathogenesis. Government expenditure on healthcare has achieved an all-time peak, despite the obvious need for improved patient-physician communication. In the areas of smartphone applications, computer appliances, online websites, cybersecurity, and other areas, such techniques have already been used. A “health prediction system” deals with the problem of unexpected changes among hospital patient flows. Emergency occurrences such as “ambulance arrivals during natural catastrophes” and “motor vehicle accidents”, as well as normal outpatient demand, generate demand for healthcare services in many hospitals. These healthcare related issues can be managed by using ML algorithms. In order to collect important information, this research paper has considered primary quantitative or survey method by considering 78 healthcare professionals to get proper information.