Disease Predictor Based on Symptoms Using Machine Learning (original) (raw)

Disease Prediction using Python

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

Disease Prediction based on Symptoms with Machine Learning is a system that predicts diseases based on the user's knowledge of clinical manifestations, ensuring solid conclusions based on such facts. Given how essential the health industry is in treating prescribers' difficulties. This method can be used to learn a little bit about small illnesses if the user only needs to be aware of the illness's basics and the patient isn't in any danger. It's a system that offers clients medical guidance and strategies, as well as a tool to help them identify their illness using this forecast. The healthcare industry as well as those who don't wish to visit a hospital or clinic for their initial diagnosis. By just entering the side effects and other crucial information, the user can learn a great deal about the illness that has been revealed to him or her, and the health sector can profit from this strategy by simply asking the patient for symptoms and providing a diagnosis. We employed machine learning techniques, Python programming with the Tkinter interface, and a dataset collected from hospitals to achieve Illness Prediction based on Symptoms.

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.

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.

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.

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 and Diagnosis Using Machine Learning

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

With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care, and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. Healthcare is very important to lead a good life. However, it is very difficult to obtain the consultation with the doctor for every health problem. The idea is to create a medical Chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. This will help to reduce healthcare costs and improve accessibility to medical knowledge through medical Chatbot. The chatbots are computer programs that use natural language to interact with users. The Chatbot stores the data in the database to identify the sentence keywords and to make a query decision and answer the question.

Prediction of Symptom Based Health Cautionary by using Machine Learning

CGC International Journal of Contemporary Technology and Research

Conceptual - Machine learning is the subset of man-made reasoning that goes under information science. Without expressly customized, getting PCs to learn is a science known as Machine Learning. The proposal frameworks present in the market are believed to be working in popular applications like YouTube web-based media applications like Facebook, Instagram or item based applications like Flipkart. Essentially, these frameworks help to focus on data that is concerned or valuable for a specific client. One area where such frameworks can be exceptionally helpful is infection cautioning system. In light of an illness the client contributions to the framework, that he thinks they are inclined to or they are experiencing they will be proposed top 5 or top 3 sicknesses they are generally inclined to dependent on the likeness between the infection client inputted and the illness client is being suggested for this situation being cautioned. As of now, everything is accessible on the web, each...

DISEASE PREDICTION SYSTEM USING SYMPTOMS

IRJET, 2023

Technology has revolutionized the health domain largely. This project aims to design a diagnostic model for various diseases relying on their symptoms. This System has used data mining techniques such as classification in order to achieve such a model. Datasets consisting of voluminous data about patient diseases are gathered, refined, classified, and used for training the intelligent agent. Here, the Naive Bayes Algorithm is used for classification purposes. Naïve Bayes Classifier calculates the probabilities of the disease. Based on the result, the patient can contact the doctor accordingly for further treatment. It is an exemplar where technology and health knowledge are sewn into a thread perfectly with a desire to achieve "prediction is better than cure"

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.

Disease Prediction and Treatment Recommendation Using Machine Learning

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

Despite the availability of advanced technology and easy access to information, many people still rely on traditional methods of seeking medical treatment, such as visiting hospitals and consulting doctors for even minor symptoms. However, this approach can be time-consuming and inefficient, as patients with minor illnesses can take up valuable resources that could be better used to treat more serious cases. As a result, this research proposes a new approach to disease prediction using machine learning and symptom-based analysis. The goal is to develop a predictive model that can accurately identify potential diseases based on a patient's symptoms. This model utilizes machine learning techniques to analyze and process symptom data, allowing for quick and precise disease prediction. The study uses a large dataset of patient symptoms and medical records to train and test the model, which demonstrated high accuracy in predicting diseases. The results of this study suggest that the proposed model could be a useful tool for early diagnosis and treatment of diseases, with the potential to improve healthcare outcomes. Overall, this research represents an important contribution to the field of healthcare informatics, with possible applications in disease prevention, treatment, and management.