Data Analytics for Disease Prediction

S. Menaga

Assistant Professor, Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Tiruppur, India

Corresponding Author:sri.ece09@gmail.com

Dr.G.Kalaiarasi

Associate Professor, Department of Electronics and Communication Engineering, VSB Engineering College, Karur, India

Corresponding Author:kalaiibe@gmail.com

Dr. R.Vanithamani

Professor, Department of Biomedical Instrumentation Engineering, School of Engineering, Avinashilingam Institute for Home science and Higher Education for Women, India

Corresponding Author:vanithamani_bmie@avinuty.ac.in

M.Nivetha

Assistant Professor, Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Tiruppur, India

Corresponding Author:niveathaece@gmail.com

Abirami A

Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Alathukombai, Sathyamangalam, Erode, Tamilnadu, India

Corresponding Author:abirarmia@bitsathy.ac.in

Lakshmanaprakash S

Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Alathukombai, Sathyamangalam, Erode, Tamilnadu, India

Corresponding Author:lakshmanaprakashs@bitsathy.ac.in

Abstract :

Human life in the modern era is influenced by a large number of diseases, which are the major causes of death. When patients exhibit symptoms clearly indicating abnormalities, healthcare systems can treat them. Diagnoses of intense diseases during the early stages allow patients to be treated, thus reducing their risk. In the absence of treatment, chronic conditions develop, sometimes resulting in death. Diagnosis of intensive diseases causes 59 percent of deaths annually. Medical services is a complex structure, containing a wide range of areas that are challenging to manage with excellent accuracy, while at the same time patients demand reasonable prices. In the medical services industry, fresh innovations are being incorporated. Predictive analytics and data science are changing industries because they can predict .

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doi.org/10.36647/MLAIDA/2022.12.B1.Ch014