E-Doctor : A Web Based SVMs for Automatic Identification of Health Problems (original) (raw)
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e-Doctor: A Web based Support Vector Machine for Automatic Medical Diagnosis
Procedia - Social and Behavioral Sciences, 2013
This paper proposes e-doctor; a web-based application that makes automatic diagnoses about health problems. The whole procedure is based on Support Vector Machines (SVMs), which are supervised learning models that analyze data and proceed to decisions, based on their knowledge. System administrators define specific characteristics for each health problem that can be diagnosed, and educate the SVM by entering sample files of statistical data. After that, medical staff can enter exam information about patients, and e-doctor makes an automatic diagnosis / prediction by means of answering if the patient has (or may have in the future) a specific health problem. The application can be used in cases where statistical information plays a vital role on deciding about a patient's condition. A prototype was developed and the system trained and tested for the case of heart symptoms. The results were satisfactory.
Support Vector Machine Based Disease Diagnostic Assistant
2019
There has been a huge growth both in data and computing technology which has made it easier for the development of artificial intelligent systems that are capable of learning from this data and make medical diagnosis on their own. In this paper, Support Vector Machines (SVM) are used in implementing a multi-disease diagnostic assistant application that is able to make predictions, early detections and instant diagnosis of various illness based on given patient data. The application is implemented in an easy to use graphical user interface and contains pretrained SVM models of predicting several diseases. A medical staff creates a new patient entry and enters or uploads a patient’s required diagnostic data, once done the application gives multiple diagnosis based on the diagnostic data. In case the application makes a wrong diagnosis, it can learn from its mistake through correction from the medical staff, enabling future similar diagnosis to be correct.
International Journal of Computing and Digital Systems, 2021
Millions of folks around the earth are affliction from late disease identification and diagnosis. An incredible amount of health information has been obtained by the latest technologies in digital medical services and information communication technologies. Disease diagnosis and artificially intelligent decision support systems have drawn tremendous attention from many scientists and research community worldwide. Various algorithms developed and applied with the aid of machine learning techniques that can substantially lead to the resolution of the system of health care and can help personnel involved in the early diagnosis of diseases. This research paper will propose an artificial intelligent algorithm which helps us to effectively, rapidly and accurately classify the information. The Proposed Disease Diagnosis Support Systems (DDSS) can assist clinicians to monitor the information, facilitate their evaluation by means of a preparatory treatment and decrease evaluation time per patient. The patients may inevitably be notified and recommended dietary suggestions also. This system will allow clinicians to focus on attending patients in accordance with their homeostasis. It decreases the volume of work of doctors and enables them to define patients who need to be examined more urgently or meticulously. Even with the widespread growth of such systems security of digital data and its privacy is still a major challenge yet to solve.
Human Symptoms Based on Diseases Predictor
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Many situations occur in day to day life which affects a human being. Many problems are happening in fast manner and new diseases are rapidly being created. The main objective of this project is to apply classification algorithm to predict model for occurrence of various diseases. This project work is aimed in identifying the best classification algorithm to identify the disease probability of patients. The identification of the possibility of diseases in patients is a tedious task for doctors and researchers because it requires experience and more medical tests need to be taken. The main objective of this project is to find the best classification algorithm suitable to provide accuracy improvement during classification of normal and abnormal persons. The project contains Naïve Bayes, Support vector machine and decision tree classification with their accuracy score calculation. The applied NBS, SVM, DT classification help to predict the disease with higher accuracy in the new data set. Python 3.9 is used as the coding language.
Cardiac ailment recognition using ML techniques in E-healthcare
World Journal of Advanced Research and Reviews
Heart ailments can take numerous forms, and they are frequently referred to as cardio vascular illnesses. These can range from heart rhythm problems to birth anomalies to blood vessel disorders. It has been the main cause of death worldwide for several decades. To recognize the illness early and properly manage, it is critical to discover a precise and trustworthy approach for automating the process. Processing massive amounts of data in the field of medical sciences necessitates the application of data science. Here we employ a range of machine learning approaches to examine enormous data sets and aid in the accurate prediction of cardiac diseases. This paper explores the supervised learning models of Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, in order to provide a comparison investigation for the most effective method. When compared to other algorithms, K-Nearest Neighbor provides the best accuracy at 86.89%.
Effectiveness of Support Vector Machines in Medical Data mining
Journal of Communications Software and Systems, 2015
The idea of medical data mining is to extract hidden knowledge in medical field using data mining techniques. One of the positive aspects is to discover the important patterns. It is possible to identify patterns even if we do not have fully understood the casual mechanisms behind those patterns. In this case, data mining prepares the ability of research and discovery that may not have been evident. This paper analyzes the effectiveness of SVM, the most popular classification techniques in classifying medical datasets. This paper analyses the performance of the Naïve Bayes classifier, RBF network and SVM Classifier. The performance of predictive model is analysed with different medical datasets in predicting diseases is recorded and compared. The datasets were of binary class and each dataset had different number of attributes. The datasets include heart datasets, cancer and diabetes datasets. It is observed that SVM classifier produces better percentage of accuracy in classificatio...
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.
Machine learning equipped web based disease prediction and recommender system
2021
Worldwide, several cases go undiagnosed due to poor healthcare support in remote areas. In this context, a centralized system is needed for effective monitoring and analysis of the medical records. A web-based patient diagnostic system is a central platform to store the medical history and predict the possible disease based on the current symptoms experienced by a patient to ensure faster and accurate diagnosis. Early disease prediction can help the users determine the severity of the disease and take quick action. The proposed web-based disease prediction system utilizes machine learning based classification techniques on a data set acquired from the National Centre of Disease Control (NCDC). K-nearest neighbor (K-NN), random forest and naive bayes classification approaches are utilized and an ensemble voting algorithm is also proposed where each classifier is assigned weights dynamically based on the prediction confidence. The proposed system is also equipped with a recommendation...
Disease Prediction Application based on Symptoms
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019
The usage of mobile phones in today's world is more than ever. Mobile phones are everywhere and the mobile technology is growing at an exponential rate. The capabilities of a mobile phone have made it provide us services that make human life better. One such service that mobile phones can offer us is digital healthcare. Also, it is recognized that mobile phone applications that provide healthcare solutions are trending. Such applications provide a convenient and portable healthcare solutions to all the individuals. Such applications provide a rich experience to a user and in this way, the users will come to know more about their health and body. Digital healthcare mobile applications are capable of diagnosing a disease that a patient is suffering from using his/her symptoms. This information can be used further by a medical practitioner for later on consultation.
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.