A Novel EHR Based Extraction and Association Techniques to Identify Prevalent Medical Conditions.

Author Reg. ID: TIJER_100609

Published Paper Id: TIJER2302060

Published In: Volume 10 Issue 2, February-2023

Abstract: Due to the state of the environment and human lifestyles today, people suffer from a variety of ailments. To stop such diseases from becoming extremely severe, it is crucial to recognise and anticipate them in their earliest stages. For the most part, diagnosing diseases accurately by hand is challenging for doctors. Identifying and predicting patients with more prevalent chronic illnesses is the aim of this article. This might be done by employing a state-of-the-art Ml_methods to make sure that this categorisation accurately identifies people with chronic conditions. Disease forecasting is a difficult endeavour as well. In order to forecast diseases, data mining is essential. Predicting mortality from chronic diseases sooner allows for disease prevention, which is the only approach to combat this problem. A patient has a requirement for such a model, and machine learning is highly advised in this situation. For a doctor, however, it is impossible to make a precise forecast based just on symptoms. The most difficult duty is making a diagnosis of an illness. Data mining is crucial for diagnosing the sickness and addressing this issue

Keywords: : Chronic Diseases, Machine Learning, Diseases Prediction and accuracy,deep learning ,CNN

Downloads: 000373

Page No: 435-440

Country: Bengaluru, Karnataka, India

Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2302060

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2302060

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