| dc.description.abstract | With the global population steadily increasing,
there is a growing demand for electronic medical records
due to the substantial amount of information generated
within hospitals. Handling these records physically can
prove challenging. Electronic medical records (EMRs)
have had a profound impact on the healthcare sector by
digitizing hospital records, thereby enhancing patient
care. By enabling electronic entry, maintenance, and
storage of medical data over extended periods, EMRs
contribute to improved patient care and safety. This
review examines and compares various methods and
techniques aimed at diagnosing and predicting diseases
accurately through the use of EMRs. Additionally, it
presents a comparative analysis of different approaches
available for health prediction. Recent publications were
studied to categorize these techniques into Deep
Learning (DL) Methods, Machine Learning (ML)
Methods, and Rule-Based Methods. Moreover, the review
outlines the advantages and disadvantages associated
with these diverse techniques and discusses their impact
on the healthcare industry. | en_US |
