E-Health System for Disease Prediction based on Machine Learning (original) (raw)
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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.
Automated Health Care System Using Disease Prediction Based On Symptom
News networks have a large amount of multimedia data. This data is stored in digital video (DV) cassettes or hard disks. Such storage devices are expensive and require physical space. Cloud computing provides a relatively cheaper alternative and can be accessed from anywhere. As it provides scalability, cloud computing can be used to archive multimedia data by news networks. Users can upload the data to the system. Data is compressed and stored securely in the cloud. This data can be retrieved when needed by searching for relevant attributes.
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.
Disease Prediction Application Using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The health care systems collect data and reports from the hospitals or patient's database by machine learning and data processing techniques which is employed to predict the disease so as to create reports supported the results which used for various kinds of predictions for disease and which is that the leading explanation for the human's death since past years. Medical reports and data had been extracted from various databases to predict a number of the required diseases which are commonly found in people nowadays breast cancer, heart disease and diabetes disease and make their life more critical to measure. Nowadays technology advancement within the health care industry has been helping people to create their process easier by suggesting hospitals and doctors to travel to for his or her treatment, where to admit and which hospitals are the simplest for the treating the desired disease. we've implemented this sort of system in our application to form people's life simpler by predicting the disease by inputting certain data from their reports which can give the result positive or negative supported the disease prediction they are going to be having a choice to get recommendation of best hospitals with best doctors nearby from the past users or guardians.
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.
A Survey on Smart Digital Health Care Record with Prediction of Health Condition
2020
Humans are known to be the most intelligent species on the earth and are inherently more health conscious. Since Centuries mankind has discovered various healthcare systems. To automate the process and predict diseases more correctly machine learning methods are attending popularity in research community. We need to implement machine learning methodologies to identify the best-predicted values related to the patients in their respected health condition and also need to analyze the previous health records. For that, we need to maintain a repository or the warehouse where we need to maintain digital data related to the patients and their treatment.
The Disease prediction system using Machine learning
International Journal of Engineering and Computer Science
As, rise in the field of technology machine learning is widely used in various fields. Now it has various applications on the field of health industry. It works as a helping hand for the field of health industry. By the help of various machine learning algorithms, we can make various models for predicting the results through the large amount of dataset present in medical field. This paper comprises of efficient machine learning algorithms used in predicting disease through symptoms. As, the health industry has a huge amount of data for various fields so, we want to make a system where we can use various other applications of machine learning on health industry. This all had been done to make the better medical decisions and also for rise in the accuracy. As accurate analysis of the early prediction of disease helps in the patient care and the society services. These all challenges can be easier by the help of various tools, algorithms and framework provided by the machine learning. ...
International journal of engineering research and technology, 2021
Classical diagnosis is a process in which a medical practitioner examines the body of a patient for any possible signs or symptoms of a medical condition, undergo various medical examinations, and then conclude. This is often challenging because many signs and symptoms are nonspecific. Medical facilities must be advanced so that better decisions for patient diagnosis and treatment options can be created. Machine learning allows models to rapidly analyze data, and deliver results, leveraging both historical and real-time data. Healthcare service providers can make better decisions on patient's diagnoses and treatment options, which will end up in overall improvement of healthcare services and thereby increasing patient satisfaction. By this project, we are trying to implement functionalities of machine learning in healthcare within a single system. Some cases can occur when an early diagnosis of a disease is not within immediate reach. In such cases, this system can be effectively implemented. As widely said, "Prevention is best than cure", prediction of diseases and epidemic outbreak would lead to early prevention of an occurrence of a disease. This project mainly focuses on the development of a system, or we could say an immediate medical provision that would incorporate the symptoms and other medical data collected from the patient and store them into a Smart health dataset. This dataset would then be analyzed using the Naïve Bayesian machine learning algorithm to deliver results with maximum accuracy. GPS tracking will be used to suggest nearest doctor or specialist if the patient needs referral. This system will have the capacity to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life. Patients can seek information regarding diseases, pathological tests referred, get answer to any kind of question and solve any problem related to the disease. I.
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.