A Hybrid Approach on Smart Health Prediction using Data Mining
Keywords:Classifier, Clustering, Data mining, Decision Tree, KNN, Predictive analysis, Regression, Smart prediction
The digital technology era demands the world to provide an excellent health system, in order to ensure the community to be alive and healthy. Objectives of this research paper is admin can login using his credentials, add new doctor details, add disease and its symptoms and manage data. Doctor can login with his credentials, and view appointment of patients. New user can sign up, they can login using user Id and password. Disease prediction is done when user enter symptoms. User can upload the reports. Chat instantly with doctor, they can book appointments and can give feedback about doctors. This study can be used for the data mining techniques such as medical field, research field, and educational field and various aspects. Due to the availability of computers and other regulations, huge amount of data is becoming available in medical and healthcare areas. As per the modern technology huge improvement has been made in computer field and therefore there is no need to deal with such a large amount of data at a same time. A major objective of this study is to evaluate data mining technologies in medical and healthcare applications to develop an accurate disease prediction. It is an amazing innovation which is of exorbitant interest in the current PC world. It is a sub area of PC sciences which utilizes previously existing information in different data sets to change it into new arrangement of results. It makes use of Deep learning, machine learning and database management techniques to extract new patterns from large data sets and the knowledge associated with these patterns. By using this technique data can be extracted automatically or semi automatically. The various parameters included in data mining are classifying, clustering and predictive analysis.
How to Cite
Copyright (c) 2022 N. Sushma, S. S. Greeshma, Sadanala Manasa, S. V. Bhaskar, Anidha Arulanandham
This work is licensed under a Creative Commons Attribution 4.0 International License.