An Experimental Outlook to Design and Measure Efficacy of an Artificial Intelligence based Medical Diagnosis Support System

Our medical resources are being heavily burdened with continuous population growth in the region. Efficient software is therefore urgently needed that improves our medical diagnostic system's quality. The proposed system in this research article will be useful to both hospitals and patients. This reduces the risk of human mistakes during patient enrollment. It also preserves clinician's and patient's money and time. Using this contrivance, patients can routinely monitor their risk factors. It technologically enables patients with risk warnings based on symptoms of their illness. While conclusive proof-based medicine has wider support in the field of healthcare, doctors are still difficult to defend it. In most medical fields, research is at least many years behind experience. The proposed system is intended primarily as a checklist to different types of health conditions. This uses machine learning principles and techniques for data mining. In the years to come, the Intelligent Health Technologies designed from the proposed system will become increasingly popular.