Machine Learning-Based Predictive Modeling for Diabetes and Anemia Disease

This study will investigate various machine learning algorithms to find one that increases the accuracy of predictions for blood-related illnesses such as diabetic and anemia. The research develops a prediction model based on results from blood tests to properly diagnose blood diseases like anemia and diabetes. It provides an easy-to-use Graphic User Interface (GUI) program that decreases the likelihood of Hospital Acquired Infection (HAI) and streamlines the procedure of diagnosing diabetes and anemia, hence increasing the efficiency and accessibility of medical care. In this work, numerous modules written in Python are utilized to test various machine learning techniques to improve the accuracy of predictions for anemia and diabetes. Furthermore, this study offers consumers an advantageous method for addressing and identifying these diseases. Patients can considerably benefit from early detection of both diabetes and anemia as it enables for prompt treatment and management. Identifying high-risk patients and offering prompt assistance, which lead to improved patient health outcomes, can have practical implications for healthcare systems. Early detection can save medical professionals and insurance companies money by preventing expensive hospital stays and treatments. The research's contribution is using numerous machine learning approaches for the early detection of anemia and diabetes. The method is a novel application in the healthcare industry and has not been widely described. Additionally, this initiative has the potential to uncover previously undiscovered risk factors and advance our comprehension of the underlying causes of these diseases by utilizing a significant amount of patient data.