A SURVEY ON PREDICTION OF DISEASES THROUGH DATA MINING
DOI:
https://doi.org/10.17605/OSF.IO/KS7NBKeywords:
Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), K Nearest Neighbour (KNN), Support Vector Machine (SVM), Neural Network (NN), Iterative Dichotomiser 3 (ID3), Internal Health Disease Prediction System (IHDPS), Frequent Pattern Growth (FP Growth), Multilayer Perception (MLP), Waikato Environment For Knowledge Analysis (WEKA).Abstract
A vast amount of data is bring out in the fields of health-care and diagnostics, doctors have to make an in-person contact with patience to determine the wounds, diseases and injuries by which the patient is affected. Wrong clinical decisions taken by medical practitioners can cause any harm and result in serious loss of life of patience, which is hard to afford by any hospital. To acquire a precise and cost effective treatment, technology based data mining system can be considered to make worth decision. This survey paper analyses different data related to symptoms & diseases which can be used for predicting different types of diseases. The main focal point of this project is the application of classifying and predicting a precise disease by achieving the operations on medical data generated in the field of health-care and medical. In this project an affected multi-class Naive Bayes, Decision Tree and Random Forest algorithm is used for prediction of particular disease by training it on a set of data before implementation. Data mining plays a crucial role of predicting diseases in thealth-care. In order to determine a particular disease, numerous different tests are needed to be done. This makes the whole process tedious and it can be reduced with the help of symptoms of the patients in data mining.
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