Artificial Rabbits Optimizer with Machine Learning Based Emergency Department Monitoring and Medical Data Classification at KSA Hospitals

Project Code :TCMAPY1189

Objective

The objective of this project is to develop an Artificial Rabbits Optimizer integrated with machine learning techniques for efficient monitoring of Emergency Departments (EDs) in hospitals across Saudi Arabia (KSA). Leveraging Adaboost, MLP classifier, Naive Bayes, and Gradient Boosting algorithms, the focus lies on predicting diabetes disease status using patient data. By evaluating the effectiveness of each algorithm, the aim is to provide accurate diabetes predictions, thereby enhancing healthcare decision-making and patient care within KSA hospitals.

Abstract

This study presents a novel approach utilizing Adaboost, MLP classifier, Naive Bayes, and Gradient Boosting algorithms to predict diabetes disease status from healthcare data. Patient demographics, medical history, and clinical metrics were collected and anonymized. Targeting diabetes diagnosis as the output variable, the models were trained to classify patients as diabetic or non-diabetic. Through comprehensive evaluation and comparison, the effectiveness of each algorithm was assessed. Results indicate promising potential for accurate diabetes prediction, offering valuable insights for healthcare practitioners. This research underscores the significance of machine learning techniques in enhancing healthcare decision-making and patient care.

Keywords: AdaBoost, MLP classifier, Naive Bayes, and Gradient Boosting.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.