Smart Health Care Implementation Using Naïve Bayes Algorithm
Harshitha M , Dr. B M Sagar
—Heart disease and diabetes are two most commonly found chronic disease that has become a mainstream health issue with the current lifestyle. It is essential to identify the symptoms and treat the disease at early stages. Data mining practices are used in number of applications. It is an exercise of determining a large amount of pre-existing database to produce new information. In health care system data mining renders a vital role to predict the illness with the given symptoms and classify the disease as diabetes or heart disease. The major reason of data mining in health care system is to evolve a new automated tool for determining and diffusing pertinent health care information. Here, the system is fed with various attributes. According to those attributes, the system compares the given symptoms with the actual dataset and predicts the relevant disease based on the user input. In this system, Naïve Bayes algorithm and R tool have been used for prediction and visualization. The goal is to develop a cost-effective and easily accessible healthcare system that can benefit the medical practitioners to combat the prolonged procedures of diagnosis and faster retrieval of results.
Data Mining, Heart disease, Diabetes, Symptoms, Naïve Bayes algorithm and R tool
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[Harshitha M , Dr. B M Sagar (2019) Smart Health Care Implementation Using Naïve Bayes Algorithm IJIRCST Vol-7 Issue-3 Page No-90-93] (ISSN 2347 - 5552). www.ijircst.org
MTech Student, ISE, R.V College of Engineering, Bangalore, India, (email id: Harshitham.firstname.lastname@example.org )