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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.

IRJET- Medi-Insight: A Smart Health Prediction System

IRJET, 2021

Machine learning is a powerful technique which can increase the efficiency and accuracy in disease prediction. In current scenario there is a need for efficient machine learning models that can be used in healthcare system to predict the specific diseases by monitoring the patient's symptoms over a period. But there exist very few studies pertaining to algorithms that can be used to predict a general set of diseases, not restricted to one field of medicine. Also, rise in the field of machine learning technology is widely used in various fields. Now it has various applications in the field of health industry. It works as a helping hand in the field of healthcare by analyzing the healthcare records and patient data. The aim of this study is to produce an effective application of machine learning algorithm for health prediction that can eventually shape a suitable health prediction system for patients. This project hopes to implement a system which not only predicts the disease but also provides the suitable methodologies to cure them and give information regarding those methodologies and the doctors who can treat that disease. So we are implementing Medi-Insight: A smart health prediction system with the help of Naive Bayes algorithm for disease prediction which helps to make the better medical decisions and also for rise in the accuracy. As accurate analysis of the prediction of disease helps in the patient care and the society services. Disease prediction can be easier with the help of various tools, algorithms and framework provided by the machine learning. Thus this study proposes a framework that enables clients to get suitable direction on their medical problems through a smart health prediction system.

E-Health System for Disease Prediction based on Machine Learning

The health reports of the persons including diagnostics information and medical prescriptions are provided in the form of test based case notes due to this the previous health conditions and the medicines used by the person are not known when he visits the hospital later. But storing all the health information of a person in the cloud as the soft copy reduces this problem. To achieve this each and every hospital, dispensary, labs must have an internet connection for registration of patient's data, each patient will be identified by the unique Health Id and all the data of the patient will be stored in the cloud and the data can be accessed by only the particular patient. In order to prevent and treat illness, it is critical to perform an accurate and timely analysis on any health-related problem. The ability to diagnose disease by obtaining all information from a linked Health ID combined with Machine Learning techniques will improve the system's ability to detect diseases. We believe that our diagnostic model can operate like a doctor in the earlier diagnosis of this disease, allowing for timely treatment and the preservation of life.

SmartCare: A Symptoms Based Disease Prediction Model Using Machine Learning Approach

International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2022

The breakthrough on computer-based technology has resulted in storage of a lot of electronic data in the healthcare industry. Machine Learning technology has been proven beneficial in giving an immeasurable platform in the medical field so that health care issues can be resolved effortlessly and expeditiously. Prediction of disease at early stage could help people from getting the necessary treatment on time. These days many virtual prediction models are available for the same. The existing systems either made use of only one algorithm or prediction system were capable for predicting only one disease. The maximum accuracy of the existing systems range between 52% to 88%. The algorithms used in various prediction system consisted of Linear Regression, Decision Tree, Naïve Bayes, KNN, CNN, Random Forest Tree, etc. In our project i.e., "SmartCare: A Symptoms Based Disease Prediction Model Using Machine Learning Approach", it is possible to predict more than one disease at a time. So, the user does not need to traverse many models to predict the diseases. It will help to reduce the time and cost of predicting diseases at prior stages, so as to prevent the extremities of it and thus, there is a chance of reducing mortality rate.

Smart Health Prediction Using Machine Learning

International Research Journal on Advanced Science Hub, 2021

The "Smart Health Prediction Using Machine Learning" system, based on predictive modelling, predicts the disease of patients/users on the basis of the symptoms that the user provides symptoms as an input to the system. The application has three login options: user/patient login, doctor login, and admin login.The device analyses the symptoms given by the user/patient as input and provides the likelihood of the disease as output based on the prediction using the algorithm. Smart health predictions are made by the implementation of the Naïve Bayes Classifier. The Naïve Bayes Classifier measures the disease percentage probability by considering all its features that is trained during the training phase.Exact interpretation of disease data benefits early patient/user disease prediction and provides clear vision about the disease to the user. After a prediction, the user/patient can consult a specialist doctor using a chat consulting window. It uses machine learning algorithms and database management techniques to extract new patterns from historical data. The Forecast Accuracy can improve with the use of a machine learning algorithm and the user/patient will get fast and easy access to the application.

Advanced Patient Monitoring System with Diseases Prediction System using Machine Learning

Middle East Journal of Applied Science & Technology, 2022

IoT and machine learning (ML) are becoming increasingly efficient in the medical and telemedicine areas all around the world. This article describes a system that employs latest technology to give a more accurate method of forecasting disease. This technology uses sensors to collect data from the body of the patient. The obtained sensor information is collected with NodeMcU before being transferred to the Cloud Platform "ThinkSpeak" through an ESP8266 Wi-Fi module. ThinkSpeak is a cloud server that provides real-time data streams in the cloud. For the best results, data currently saved in the cloud is evaluated by one of the machine learning algorithms, the KNN algorithm. Based on the findings of the analysis and compared with the data sets, the disease is predicted and a prescription for the relevant disease is issued.

A Review of Common Ailments Possibility Using Machine Learning

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Machine learning in healthcare helps humans to process large and complex medical datasets and then analyze them into clinical insights which can help physicians in providing better medical care. Therefore, machine learning, when implemented in the medical field can lead to increased patient satisfaction. In this research, we will try to implement the functionalities of machine learning in healthcare in a single system. Health care can be made smart with the help of machine learning. Many cases can occur when the early diagnosis of an ailment is not within reach, So, their ailment prediction cannot be effectively implemented. As widely said "Prevention is better than cure", prediction of diseases would lead to early prevention of occurrence of disease. Medical Staff are often overworked in the medical field and hence the diagnosis becomes prone to human errors and negligence. Patients should be given treatment and diagnosis that are accurate and precise. Mistreatment may result in worsening the condition of the patient and hence the need for precise diagnosis. Therefore, the application of machine learning in disease prediction is considered in this paper as the best practice to facilitate a better healthcare system and provide better treatment to a patient as soon as possible. This paper majorly focuses on the development of a web app that would work on symptoms collected from the user and medical data and store it in the system. This data then will be analyzed using different machine learning algorithms to deliver results with maximum accuracy. I.

Disease Diagnosis System using Machine Learning

Journal of Pharmaceutical Research International, 2021

The efficient use of data mining in virtual sectors such as e-соmmerсe, and соmmerсe has led to its use in other industries. The mediсаl environment is still rich but weaker in technical analysis field. There is а lot of information that саn оссur within mediсаl systems. Using powerful analytics tооls to identify the hidden relationships with the current data trends. Disease is а term that provides а large number of соnditiоns connected to the heath care. These mediсаl соnditiоns describe unexpected health соnditiоns that directly соntrоl all the оrgаns of the body. Mediсаl data mining methods such as соrроrаte management mines, сlаssifiсаtiоn, integration is used to аnаlyze various types of соmmоn рhysiсаl problems. Seраrаtiоn is an imроrtаnt рrоblem in data mining. Many рорulаr сliрs make decision trees to рrоduсe саtegоry models. Data сlаssifiсаtiоn is based on the ID3 decision tree algorithm that leads to ассurасy, data are estimated to use entrорy verifiсаtiоn methods based on сrоss-seсtiоnаl and segmentation and results are соmраred. The database used for mасhine learning is divided into 3 parts-training, testing, and finally validation. This approach uses а training set to train а model and define its аррrорriаte раrаmeters. А test set is required to test а professional model and its standard performance. It is estimated that 70% of people in India can catch common illnesses such as viruses, flu, coughs, colds etc. every two months. Because most people do not realize that common allergies can be symptoms of something very serious, 25% of people suddenly die from ignoring the first normal symptoms. Therefore, identifying or predicting the disease early using machine learning (ML) is very important to avoid any unwanted injuries.

Common Ailments Possibility Using Machine Learning

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Machine learning in healthcare helps humans to process large and complex medical datasets and then analyze them into clinical insights which can help physicians in providing better medical care. Therefore, machine learning, when implemented in the medical field can lead to increased patient satisfaction. In this research, we will try to implement the functionalities of machine learning in healthcare in a single system. Health care can be made smart with the help of machine learning. Many cases can occur when the early diagnosis of an ailment is not within reach, So, their ailment prediction cannot be effectively implemented. As widely said "Prevention is better than cure", prediction of diseases would lead to early prevention of occurrence of disease. Medical Staff are often overworked in the medical field and hence the diagnosis becomes prone to human errors and negligence. Patients should be given treatment and diagnosis that are accurate and precise. Mistreatment may result in worsening the condition of the patient and hence the need for precise diagnosis. Therefore, the application of machine learning in disease prediction is considered in this paper as the best practice to facilitate a better healthcare system and provide better treatment to a patient as soon as possible. This paper majorly focuses on the development of a web app that would work on symptoms collected from the user and medical data and store it in the system. This data then will be analyzed using different machine learning algorithms to deliver results with maximum accuracy.

Health prediction system using machine learning

International journal of health sciences

The emergence of the coronavirus (covid-19) pandemic has substantially elevated the worldwide demand for the healthcare system. Massive numbers of elderly and prone human beings are scuffling to fitness situations such as high blood pressure, diabetes, heart attack, and so on. Here in our project, I am making healthcare with the help of an algorithm and deep learning method to predict the disease. A user interacts with the system just like one interacts with his doctor and based on the symptoms provided by users and the system will identify the symptom and predict the disease. Thus, target to layout and implement a low- priced and smart healthcare system that allows non-stop assessment and tracking of patient fitness, thus BP and frame temperature monitoring is critical for that I used sensors that transmit information over a wi-fi network via a wi-fi module that allows fact analytics and visualization by using healthcare workforce.