Heart Diseases Forecast using Data Mining Techniques and Tools
Karan Khayani1, Sanchit Arora2, I. Mala Serene3
1Karan Khayani, School of Information Technology and Engineering, VIT Deemed to be University, Vellore, India.
2Sanchit Arora, School of Information Technology and Engineering, VIT Deemed to be University, Vellore, India
3Mala Serene I*, School of Information Technology and Engineering, VIT Deemed to be University, Vellore, India.
Manuscript received on November 16, 2019. | Revised Manuscript received on 27 November, 2019. | Manuscript published on December 10, 2019. | PP: 2726-2729 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6594129219/2019©BEIESP | DOI: 10.35940/ijitee.B6594.129219
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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: Data Mining have always been a field and combination of both computer science and statistical knowledge. From the beginning it is used to ascertain designs, patterns and arrangements which are formed in the information pool. The motive of the data mining development is to produce useful information from the pool of raw data and convert it into useful information which can be used for future arrangements. The tools which are used in data mining are helpful in predicting the future trends and predictions across the market, which also help in decision making and building the knowledge to make decisions. The “Healthcare Industry” is generally information rich. It has been collecting data to improve the continuing problems and help to identify the solutions for that problems. Data mining techniques can be used to predict heart conditions from the voluminous and complex data which are kept by the hospitals for decision making which are difficult to analyze by outmoded methods. Unfortunately, outmoded methods are less accurate in discovering hidden information from effective decision making. Data mining helps in altering the huge amount of data into knowledge driven which takes, as compared to others, less time and effort for the prediction and with greater accuracy. Our effort is to apply different data mining techniques that are used to solve the problem of biased forecasts and decision making and help in calculating the results with more accuracy.
Keywords: Heart Disease, Data Mining, Naïve Bayes, K-Nearest Neighbor, Decision Tree
Scope of the Article: Data Mining