Published Paper Details:

Due to the ever-increasing demands for high-quality medical treatment, the ever-increasing expenses, and the imperative to make effective use of available resources, healthcare systems all over the world are confronted with issues that have never been seen before. Taking this into consideration, machine learning (ML) has emerged as a potentially useful instrument that has the potential to improve the efficiency and efficacy of health systems. This article proposes a complete framework for employing machine learning techniques to address critical difficulties in the healthcare industry. The primary aim of this framework is to improve operational efficiency, resource allocation, and patient outcomes from a healthcare perspective. In order to demonstrate how machine learning may be smoothly incorporated into healthcare processes, the framework that has been suggested incorporates data gathering, preprocessing, model creation, deployment, and continual improvement. This is our novel. In addition to shedding light on its characteristics, the technique enables users to study and analyse the user needs and determine what they need. both the function of objects associated to the system and the machine learning methods that must be implemented to for the dataset. In the course of our investigation, we made use of a dataset that included actual data that was first obtained from a medical facility. run by the government of Palestine for the past three years (since the beginning). The SEMLHI technique contains seven There are several phases, including designing, implementing, maintaining, and designing workflows; organizing information; and ensuring The release of the software applications, as well as the testing and assessment of performance, security, and privacy.