Intelligent Health Risk and Disease Prediction Using Optimized Naive Bayes Classifier (original) (raw)
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Disease Prediction using Naïve Bayes -Machine Learning Algorithm
https://ijshr.com/IJSHR\_Vol.6\_Issue.4\_Oct2021/IJSHR-Abstract.04.html, 2021
It can occur on many occasions that you or a loved one requires urgent medical assistance, but they are unavailable due to unforeseen circumstances, or that we are unable to locate the appropriate doctor for the care. As a result, we will try to incorporate an online intelligent Smart Healthcare System in this project to solve this issue. It's a web-based programmed that allows patients to get immediate advice about their health problems. The aim of the smart healthcare system is to create a web application that can take a user's symptoms and predict diseases, as well as serve as an online consultant for various diseases. We created an expert system called Smart Health Care System, which is used to make doctors' jobs easier. A machine examines a patient at a basic level and recommends diseases that may be present. It begins by inquiring about the patient's symptoms; if the device is able to determine the relevant condition, it then recommends a doctor in the patient's immediate vicinity. The system will show the result based on the available accumulated data. We're going to use some clever data mining techniques here. We use several intelligent data mining techniques to guess the most accurate illness that could be associated with a patient's symptoms, and we use an algorithm (Naive Bayes) to map the symptoms with potential diseases based on a database of many patients' medical records. This system not only makes doctors' jobs easier, but it also benefits patients by getting them the care they need as soon as possible.
Disease Prediction System using naïve bayes
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Accurate and on-time analysis of any health-related problem is vital for the prevention and treatment of the illness. The standard way of diagnosis might not be sufficient. Developing a diagnosis system with machine learning (ML) algorithms for prediction of any disease can helpin a very more accurate diagnosis than the traditional method. The proposed model is an Disease Prediction System with the help of machine learning algorithm Naive Bayes which takes the symptoms as the input and it gives the output as predicted disease. It results in saving time and also makes it easy to induce a warning about your health before it's too late. By using this model anyone can get the result as predicted disease by simply given the symptoms as input. The accuracy of this model is more than existing models.
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
This work is dedicated to patients specially, rural patients can get to know the early stage detection of diseases before laboratory tests reducing the unlimited waiting time and cost expenditure. Clinical Decision Support System (CDSS) can be used for analyzing diseases to predict almost accurate disease automatically and patient’s query. This work has been done with the help of a doctor as a human expert. We collected 300 sample data from patients. We have made the dataset from our sample patient’s information. Naïve Bayes classifier is used here to classify the diseases easily. The selected diseases are Malaria, Tuberculosis, Stroke, Fever, Diabetes, Heart disease. The prediction of a disease is measured with the prediction of a doctor before laboratory tests to get the system’s accuracy. Here we got 100% accuracy on the trained dataset containing 180 cases
Health Prediction and Medical Diagnosis using Naive Bayes
IJARCCE, 2017
In today's modern world, many of us are using computer and its applications for various works. Lot of people are using it in their daily life. Use of computer applications in medical field is also significant. It can provide user an interface for medical guidance. Platform independent system can help people to use it on their computers and will provide users an instant guidance on their health issues.System will use Naïve Bayes algorithm and depending on the symptoms will predict the diseases and for normal person it will predict the daily hygiene diet and routines which he can follow. Users will also be able to contact the specialist doctors nearby. It will help users for easy medical treatment and diagnosis.
Diseases Prediction Model using Machine Learning Technique
International Journal of Scientific Research in Science and Technology, 2021
Now a day, people face various diseases due to the environmental condition and living habits of them. So prediction of disease at earlier stage becomes important task. But the prediction on the basis of symptoms becomes too difficult for doctor. The correctly prediction of disease is most challenging task. To overcome this problem data mining plays an important and efficient way to predict the disease. Medical science has huge amount of data growth per year. Due to increase amount of data growth in medical and healthcare field the accurate analysis on medical data which has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in the large amount of medical data. We have designed the heart disease prediction system. We proposed multiple disease prediction based on symptoms of the patient. For the heart disease prediction, we used knn , naïve bayes machine learning algorithm for accurate prediction of disease. For disease prediction required disease symptoms dataset. Here we focused on heart disease prediction, because the heart disease is one of the leading causes of death among all other diseases. The heart disease prediction contains that whether the patient suffer from heart disease or not by using naïve bayes and KNN algorithm. In this heart disease prediction, the living habits of person and checkup information consider for the accurate prediction. The accuracy of heart disease prediction by using naïve bayes is 94.5% which is more than KNN algorithm. And the time and the memory requirement is also more in KNN than naïve bayes. After heart disease prediction, this system able to gives the risk associated with heart disease which is lower risk of heart disease or higher. For the risk prediction, we are using CNN algorithm.
Smart Healthcare Prediction System Using Machine Learning
In this paper, we have introduced the techniques and applications of machine learning in the healthcare system. We know that day by day large amount of data is generating in healthcare industry and other industries as well. Such large amount of data cannot be processed by humans manually in a short time to make diagnosis of diseases and treatments. To reduce this manual work, we have explored data management techniques and machine learning algorithms in healthcare applications to develop accurate decisions. It also gives the detailed description of medical data which improves various aspects of healthcare applications. It is the latest powerful technology that will reduce the manual work of professionals. In this paper, we will be using the Naïve Bayes machine learning algorithm to train our machine to predict the different types of diseases. It uses existing medical information in various databases to rework it into new results and researches. It will extract the new patterns from large datasets to make prediction and knowledge associated with these patterns. Particularly, the important task is to get data by means of automatic or semi-automatic.
Prediction Support System for Multiple Disease Prediction Using Naive Bayes Classifier
The prediction support system extracts the personal data such as user health conditions from day to day life. The lifestyle data are gathered and stored at data repository by using web technology and mobile applications. The user enter their daily health conditions in textual format. The Natural Language Processing (NLP) is used to understand the given input and further forecast the user's illness. The keyword is extracted using text mining algorithm. In this project an effective Multiclass Naive Bayes algorithm is used for predicting the multiple disease by implementing the operations on medical datasets.
Heart Disease Prediction System Using Naïve Bayes
CERN European Organization for Nuclear Research - Zenodo, 2022
As large amount of data is generated in medical organisations (hospitals,medical centers) but as this data is not properly used. There is a wealth of hidden information present in the datasets. This unused data can be converted into useful data. For this purpose we can use different data mining techniques. This paper presents a classifier approach for detection of heart disease and shows how Naive Bayes can be used for classification purpose. In our system, we will categories medical data into five categories namely no,low, average,high and very high.Also, if unknown sample comes then the system will predict the class label of that sample. Hence two basic functions namely classification (training) and prediction (testing) will be performed. Accuracy of the system is depends on algorithm and database used.
Real Time Diabetes Prediction using Naïve Bayes Classifier on Big Data of Healthcare
2020
1,2,3U.G. Students, Department of Information Technology, TSEC College, Mumbai, Maharashtra, India 4Professor and Head of Department , Examination Incharge, Department of Information Technology, TSEC College, Mumbai, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Diabetes is a chronic disease, with numerous cases enrolled annually. The number of deaths caused by diabetes has been expanding each year, and it is crucial to anticipate the factor so that they can be relieved at the soonest guaranteeing the patient's life is saved. This prediction is effectively acquired by utilizing Naïve Bayes Classifier. This algorithm classifies, based on the indications of whether an individual has diabetes or not. This model achieved an accuracy of around 81%. The proposed system also supports live streaming of data input, where the results are obtained in real-time for the e...
Decision Support in Heart Disease Prediction System using Naive Bayes
Data Mining refers to using a variety of techniques to identify suggest of information or decision making knowledge in the database and extracting these in a way that they can put to use in areas such as decision support, predictions, forecasting and estimation. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not "mined" to discover hidden information for effective decision making. Discovering relations that connect variables in a database is the subject of data mining. This research has developed a Decision Support in Heart Disease Prediction System (DSHDPS) using data mining modeling technique, namely, Naïve Bayes. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It is implemented as web based questionnaire application. It can serve a training tool to train nurses and medical students to diagnose patients with heart disease.