Prevasive Healthcare and Machine Learning Algorithms (original) (raw)
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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.
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.
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.
Chronic Diseases System Based on Machine Learning Techniques
International Journal on Data Science
This paper aims to improve the quality of the patient's life and provide them with the lifestyle they need. And we have the intention to obtain this by creating a mobile application that analyzes the patient's data such as diabetes, blood pressure, and kidney. Then, implement the system to diagnose patients of chronic diseases using machine learning techniques such as classification. It's hard for the patients of chronic diseases to record their measurements on a paper every time they measure either the blood pressure or sugar level or any other disease that needs periodic measurements. The paper might be lost, and this can lead the doctor not fully to understand the case. So, the application is going to record measurements in the database. Also, it's difficult for patients to decide what to eat or how many times they should exercise according to their situation. Our idea is to recommend a lifestyle for the patient and make the doctor participate in it by writing not...
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Artificial intelligence (AI) has been widely used in many sectors like Agriculture and Farming, Autonomous Flying, Security and Surveillance, Clinical Medicine etc, and one such important sector is healthcare where there is a tremendous increase in innovations in the fields of AI.Medical facilities need to be really advanced so that better decisions can be made for patient diagnosis and treatment options. Machine learning in healthcare helps humans to process huge and complex medical datasets and then analyse them into clinical insights. This can be later used by the physicians in providing suitable medical care. Hence machine learning and artificial intelligence when implemented in healthcare can lead to increased patient satisfaction. Disease Prediction using AI is the system that is used to predict the diseases from the symptoms which are given by the patients or any user. The system then processes the symptoms provided by the user be it image or details as an input and gives the required output depending upon the probability of the disease. With an increase in biomedical and healthcare data, accurate analysis of medical data benefits early disease detection and patient care. By using this, we are predicting diseases like Diabetes, Malaria, Heart disease and many more.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019
Nowadays the advances in the computer technology have validated great development on healthcare technologies in numerous fields. However these new technologies have made also healthcare data not only much bigger but also much more difficult to handle and process. Currently the peoples are leading to death because of the proper distribution of medical resources over the world. Cloud and big data not only are important techniques but also are gradually becoming the trends in healthcare innovation. However these problems can be greatly solved by building a healthcare system with the help of these new technologies. But the greatest challenge of building a comprehensive healthcare system is in the handling of the heterogeneous healthcare data captured from multiple sources. In-order to provide a more convenient service and environment of healthcare, this paper proposes a big health application system based on the health internet of things and big data. The world is confronting issues, for example, uneven conveyance of restorative assets, the developing endless maladies, and the expanding restorative costs. Mixing the most recent data innovation into the human services framework will enormously alleviate the issues. So building huge well being application framework by adequately coordinating medicinal well being assets utilizing keen terminals, well being Internet of Things (IoT), enormous information and distributed computing is the significant method to unravel the above issues. Also in this work proposes a new convolutional neural network based multi-modal disease risk prediction (CNN-MDRP) algorithm using structured and unstructured data from hospital.
—The paradigm change from delayed interventional to Predictive, Preventive and Personalized Medicine is a leading global challenge in the 21st century. Ubiquitous convergence of mobile applications, new intelligent sensors and machine learning methods make possible creation of new generation personalized automatic healthcare monitoring and pathologies detection systems. These systems will help to make platforms for more effective treatments tailored to the person, that is considered as the "medicine of the future". Implementation of these latest trends in medicine will make it possible to detect health deterioration remotely and will avoid millions of hospitalizations costing billions of dollars in the world every year. In the article the problem of cardiovascular diseases and the state of healthcare industry is described and general architecture of automatic system for heart pathologies detection is proposed.
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.
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.