International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 04 | Apr 2022
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
SMART HEALTHCARE PREDICTION Asha Bharambe1,Simran Nara2,Manju Paryani3 ,Karan Sadhwani4 1
Professor, Dept. of Information Technology, VESIT, MUMBAI Student, Dept. of Information Technology, VESIT, MUMBAI ---------------------------------------------------------------------***----------------------------------------------------------------2,3,4
Abstract - We have been afraid to go to the doctor in recent
years because of the COVID’s situation. So for minor diseases, we planned to create a web application that will be helpful for people. Our motive is to create a system for the welfare of people. In this paper, we aim to predict user’s diseases based on their symptoms. We implement the Decision Tree Algorithm to reach our goal, which helps to determine the patient’s health condition after collecting their symptoms by predicting the disease. This web application can determine and extract previously unseen patterns, relations, and concepts related to multiple diseases from historical database records of specified multiple diseases. The paper presents an overview of the data mining techniques with their applications in the healthcare field. In health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available. Such a large amount of data cannot be processed by humans in a short time to make a diagnosis. One of the main objectives is to examine data mining techniques in healthcare applications in order to make the best decisions possible. Keywords- Data Mining, Healthcare, Decision Tree, Symptoms, and Web Application
I. INTRODUCTION Health is one of our most valuable assets, and it has a direct impact on every sort of progress or development. Most people ignore this asset in today’s hectic world, which may be due toa lack of time and the complexities of huge material available on the Internet. Data mining has brought a number of techniques that can help us fix this problem and concentrate on both health and work at the same time. In the modern period, Because there is a need for an effective analytical process for finding unknown and useful information in health data, data mining is becoming increasingly popular in the healthcare area. In the health industry, Data mining has various advantages in the health industry, including detection of health insurance fraud, and the availability of medical solutions to patients at lower cost, detection of causes of diseases, and identification of medical treatment methods. It also assists healthcare researchers in building effective healthcare policies, drug recommendation systems, individual health profiles, etc.
II. RELATED WORK
Bhagyashree Pati presented a paper that Data Mining helps us to understand a huge amount of unmined data is collected by the healthcare industry in order to discover hidden information for effective diagnosis and decision making. Data mining is the process of extracting hidden information from a massive dataset, categorizing valid and unique patterns in data. It might have happened so many times that you or someone needed doctor help but they are not available due to some reason. The health management system is a project that provides end-user support and digital consultation. Authors have proposed a system that allows users to get guidance on their health issues through intelligent health care online system. The main objective of the paper is to predict Chronic Kidney Disease (CKD),Heart Disease and Liver Disease using clustering technique, K-means algorithm. In the paper [2], the author G.Pooja reddy, M.Trinath Basu, K.Vasanthi, K.Bala Sita Ramireddy, Ravi Kumar Tenalihave offered a framework that examines a patient at a basic level and suggests diseases that might be present. It starts by gathering information from the patient about their symptoms, and if the framework can identify the appropriate illness, it then recommends a specialist to the patients. On the off chance that the framework isn’t sufficiently sure, it asks a few questions to the patients. When the system has accessed full data from the patient, then the framework will demonstrate the result. In the paper [3], the author Pinky Saikia Dutta, Shrabani Medhi, Sunayana Dutta, Tridisha Das, Sweety Buragohain have presented a paper which is a webbased application for Predicting diseases based on user input symptoms. It mines data sets to forecast likely diseases and delivers remedial methods for Effective Treatment. The algorithm implemented in this paper is the Apriori algorithm by generating only one candidate set. This is due to the fact that our goal is to predict only one disease from a group of symptoms. In FP tree Generation, Item sets are considered in order of their descending value of support count. An item header table is created to make tree navigation easier, with each itempointing to its occurrences in the tree via a chain of node connections.
In the paper [1], the author Subasish Mohapatra, Prashanta Kumar Patra, Subhadarshini Mohanty,
In the paper [4], the author Nikita Kamble, Manjiri Harmalkal, Manali Bhoir, Supriya Chaudhary, paper is Based on available cumulative information, the system
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