Heart Disease Risk Predictor
Rashmi Mishra1, Pooja Saharan2, Amrita Jyoti3
1Rashmi Mishra, Department of Computer Science & Engineering, ABESEC, Ghaziabad, India.
2Pooja Saharan, Department of Computer Science & Engineering, ABESEC, Ghaziabad, India.
3Amrita Jyoti, Department of Computer Science & Engineering, ABESEC, Ghaziabad, India.
Manuscript received on 06 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 701-705 | Volume-8 Issue-10, August 2019 | Retrieval Number: J88720881019/2019©BEIESP | DOI: 10.35940/ijitee.J8872.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cardiovascular disease is one of the focused areas is medical area because its origins sickness and death amongst the population of the entire world. Data mining techniques play an important role to convert the large amount of raw data into meaningful information which will help in prediction and decision of Cardiovascular disease. The prediction models were technologically advanced using diverse amalgamation structures and sorting techniques such as k-NN, Naive Bayes, LR, SVM, Neural Network, Decision Tree. It is very necessary for the recital of the prediction models to choose the exact amalgamation of momentous features. The main Aim of the propose System is to develop an develop an Intelligent System using data mining modeling technique. The proposed system retrieves the data set and compare the data set with the predefined trained data set. The existing decision support system cannot predict the complex question for diagnosing the heart disease but the proposed system predicts the complex queries which will help and assist the healthcare practitioners to take appropriate decisions. This proposed system aims to provide a web platform to predict the occurrences of disease on the basis of various symptoms. The user can select various symptoms and can find the diseases with their probabilistic figures.
Keywords: Naive Bayes, Heart disease, Data Mining, JDK, Thal
Scope of the Article: Data Mining