A Review of Common Ailments Possibility Using Machine Learning (original) (raw)

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.

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.

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.

Disease Predictor Based on Symptoms Using Machine Learning

International Journal for Research in Applied Science and Engineering Technology

Given how important the health sector is in curing prescribers' problems, Disease Prediction based on Symptoms with Machine Learning is a system that predicts diseases based on the user's knowledge of clinical manifestations, assuring solid findings based on such facts. If the user simply has to know a little bit about the sickness and the patient isn't in any danger, this technique can be used to learn a little bit about minor ailments. It's a system that provides medical advice and tactics to clients, as well as a tool to help them figure out what ailment they have utilizing this forecast. It's also a big benefit for the healthcare sector as well as individuals who don't want to travel to a hospital or clinic for their initial diagnosis. The user can learn a lot about the condition that has been revealed to him or her by simply inputting the side effects and other critical information, and the health sector can benefit from this method by simply asking the patient for symptoms and giving them a diagnosis. To achieve Disease Prediction based on Symptoms, we used Machine Learning techniques, Python Programming with Tkinter Interface, and a dataset acquired from hospitals. The phrases Disease Predictor, Machine Learning, and Tkinter Interface are used in this research.

Application of Machine Learning in Disease Prediction

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Healthcare is a sector that is always changing. Healthcare professionals may find it challenging to stay current with the constant development of new technologies and treatments. As a result, the purpose of this research paper is to try and implement machine learning features in a specific system for health facilities. Knowing if we are ill at an early stage rather than finding out later is crucial. The entire process of treatment can be made much more effective if the disease is predicted ahead using specific machine learning algorithms as opposed to directly treating the patient. In this work, disease is predicted based on symptoms using machine learning. Machine learning algorithms like Naive Bayes, Decision Tree, Random Forest, and KNN are used to forecast the disease on the provided dataset. As you can see, there are numerous potential applications of machine learning in clinical care in the areas of patient data improvement, diagnosis, and treatment, cost reduction, and improved patient safety.

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. ...

Disease Prediction using Machine Learning

international journal for research in applied science and engineering technology ijraset, 2020

Machine learning has various applications and one of them is healthcare. There should be much more advanced medical facilities so as to provide the best possible treatment for the patients[3].Also there are many machine learning algorithms (such as KNN, Random Forest and Decision Tree Classifier algorithms and many more) which were selected and on the given data many algorithms were applied so as to produce the best results. We can say that when machine learning implemented in healthcare can lead to a high increase in patient satisfaction. so this research paper, will try to implement functions of machine learning in health facilities in a particular system[8]. Instead of directly performing treatment for the patient, if the disease is predicted beforehand using certain machine learning algorithms then the entire process of treatment can be made much more efficient[12]. There are also some cases which occur when early diagnosis of a disease is not performed or carried out. Hence disease prediction is a really important step while treating the patient. As it is said "Prevention is better than cure", the right prediction of disease would definitely lead to an early prevention of that particular disease[19].

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.

Medical Disease Prediction using Machine Learning Algorithms

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

There is a growing importance of healthcare and pandemic has proved that healthcare is an important aspect of an individual life. Most of the medical diagnoses require going to the doctor and fixing appointments for a consultation and sometimes to get accurate disease indications we have to wait for blood reports also we have to travel long distances to seek doctor consultation. When we are not feeling well the first thing we do is to check our temperature to get an estimate or baseline idea of our fever so we can consult our doctor if the temperature is high enough similarly a medical disease prediction application can be used to get a baseline idea of disease and can indicate us whether we should take immediate doctor consultation or not, or at least start some home-remedies for the same to find temporary relief. Combining machine learning with an application interface to interact with users provides opportunities for easy interaction with the users with the machine learning model to get more accurate predictions. Sometimes people feel reluctant to visit a hospital or consult a doctor for minor symptoms but there are cases where these minor symptoms may be indications of severe health problems hence medical disease prediction maybe useful to get a baseline prediction or estimation of disease in such cases.

"Detection of Diseases using Machine Learning"

IRJET, 2022

The world's growing population has put enormous pressure on the healthcare sector to offer highquality treatment and accommodations. Artificial intelligence and Machine Learning are no exceptions in the healthcare industry, which has long been a vigorous adherent of cutting-edge technology. We have developed a web application using flask framework. It consists of web pages designed for different functionality. It is a disease prediction system which can be deployed on any network for communication among ecumenical users. Report is generated that can be subsidiary in-order to keep records. The report generated can be downloaded locally on user's device as well as provided on user's personal emails.