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Designing Disease Prediction Model Using Machine Learning Approach - 2019

Designing Disease Prediction Model Using Machine Learning Approach

Research Paper on Designing Disease Prediction Model Using Machine Learning Approach

Research Area:  Machine Learning


Now-a-days, people face various diseases due to the environmental condition and their living habits. So the prediction of disease at earlier stage becomes important task. But the accurate prediction on the basis of symptoms becomes too difficult for doctor. The correct prediction of disease is the most challenging task. To overcome this problem data mining plays an important role to predict the disease. Medical science has large amount of data growth per year. Due to increase amount of data growth in medical and healthcare field the accurate analysis on medical data which has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in the huge amount of medical data. We proposed general disease prediction based on symptoms of the patient. For the disease prediction, we use K-Nearest Neighbor (KNN) and Convolutional neural network (CNN) machine learning algorithm for accurate prediction of disease. For disease prediction required disease symptoms dataset. In this general disease prediction the living habits of person and checkup information consider for the accurate prediction. The accuracy of general disease prediction by using CNN is 84.5% which is more than KNN algorithm. And the time and the memory requirement is also more in KNN than CNN. After general disease prediction, this system able to gives the risk associated with general disease which is lower risk of general disease or higher.

Disease Prediction
Machine Learning
K-Nearest Neighbor (KNN)
Convolutional neural network (CNN)
Deep Learning

Author(s) Name:  Dhiraj Dahiwade; Gajanan Patle; Ektaa Meshram

Journal name:  

Conferrence name:  International Conference on Computing Methodologies and Communication (ICCMC)

Publisher name:  IEEE

DOI:  10.1109/ICCMC.2019.8819782

Volume Information: