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  1. Worst Case, Average Case, and Best Case

    research paper on disease prediction using machine learning

  2. The general model for Parkinson's disease processing and prediction.

    research paper on disease prediction using machine learning

  3. Disease Prediction and Diagnosis Using Machine Learning

    research paper on disease prediction using machine learning

  4. (PDF) Study of Machine Learning Algorithms for Prediction and Diagnosis of Cardiovascular

    research paper on disease prediction using machine learning

  5. (PDF) Liver Disease Prediction using Machine learning Classification Techniques

    research paper on disease prediction using machine learning

  6. (PDF) THE PREDICTION OF DISEASE USING MACHINE LEARNING

    research paper on disease prediction using machine learning

VIDEO

  1. Heart Disease Prediction Project using Machine Learning

  2. Mental Disease Prediction

  3. Mental Disease Prediction

  4. “IoT-Based Disease Prediction using Machine Learning”

  5. Liver Disease Prediction Using Machine Learning

  6. Potato Disease Prediction Using Machine Learning Techinque

COMMENTS

  1. Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

    Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis: 257 : Random forest-based similarity measures for multi-modal classification of Alzheimer's disease: 248 : Effective Heart disease prediction Using hybrid Machine Learning techniques: 214

  2. (PDF) Disease Prediction Using Machine Learning

    Disease Prediction Using Machine Learning. * Research Gate Link: Marouane Fethi Ferjani. Computing Department. Bournemouth University. Bournemouth, England. [email protected]. Abstract ...

  3. Identification and Prediction of Chronic Diseases Using Machine

    This paper proposed a method of identification and prediction of the presence of chronic disease in an individual using the machine learning algorithms such as CNN and KNN. The advantage of the proposed system is the use of both structured and unstructured data from real life for data set preparation, which lacks in many of the existing approaches.

  4. Machine learning prediction in cardiovascular diseases: a meta-analysis

    Most importantly, pooled analyses indicate that, in general, ML algorithms are accurate (AUC 0.8-0.9 s) in overall cardiovascular disease prediction. In subgroup analyses of each ML algorithms ...

  5. Development of machine learning model for diagnostic disease prediction

    The numbers of disease prediction papers using XGBoost with medical data have increased recently 33,34,35,36. XGBoost is an algorithm that overcomes the shortcomings of GBM (gradient boosting ...

  6. Early-Stage Alzheimer's Disease Prediction Using Machine Learning

    Using machine learning and deep learning platforms, this study aims to combine recent research on four brain diseases: Alzheimer's disease, brain tumors, epilepsy, and Parkinson's disease. By using 22 brain disease databases that are used most during the reviews, the authors can determine the most accurate diagnostic method.

  7. Popular deep learning algorithms for disease prediction: a review

    6 Conclusion. This paper reviews the deep learning algorithms in the field of disease prediction. According to the type of data processed, the algorithms are divided into structured data algorithms and unstructured data algorithms. Structured data algorithms include ANN and FM-Deep Learning algorithms.

  8. Prediction of Cancer Disease using Machine learning Approach

    ChaoTan et al [1] explored the feasibility of using decision stumps as a poor classification method and track element analysis to predict timely lung cancer in a combination of Adaboost (machine learning ensemble). For the illustration, a cancer dataset was used which identified 9 trace elements in 122 urine samples.

  9. Disease Prediction From Various Symptoms Using Machine Learning

    Developing a medical diagnosis system based on machine learning (ML) algorithms for prediction of any disease can help in a more accurate diagnosis than the conventional method. We have designed a disease prediction system using multiple ML algorithms. The data set used had more than 230 diseases for processing.

  10. Disease Prediction using machine learning algorithms

    Comparatively, supervised machine learning (ML) algorithms has shown notable capability in exceeding standard approach for disease detection and helps medical experts in the early detection of high-risk diseases. In this paper, algorithms discussed were K- Nearest Neighbor, Naïve Bayes, Support Vector Machine and Decision Trees.

  11. Disease Prediction using Machine Learning Algorithms

    This research work carried out demonstrates the disease prediction system developed using Machine learning algorithms such as Decision Tree classifier, Random forest classifier, and Naïve Bayes classifier. The paper presents the comparative study of the results of the above algorithms used.

  12. Early and accurate detection and diagnosis of heart disease using

    Validation of the prediction model is an essential step in machine learning processes. In this paper, the K-Fold cross-validation method is applied to validating the results of the above-mentioned ...

  13. Chronic kidney disease prediction using machine learning techniques

    This study focuses on chronic kidney disease prediction using machine learning models based on the dataset with big size and recent than online available dataset collected from St. Paulo's Hospital in Ethiopia with five classes: notckd, mild, moderate, severe, and ESRD and binary classes: ckd and notckd by applying machine-learning models.

  14. Machine Learning and Prediction of Infectious Diseases: A Systematic Review

    The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning. This study was carried out following the guidelines of the Cochrane Collaboration and the meta-analysis of observational studies in epidemiology and the preferred reporting items for systematic reviews and meta-analyses. The suitable bibliography on PubMed/Medline ...

  15. Multiple disease prediction using Machine learning algorithms

    Framework for multiple disease prediction. In this framework, machine learning algorithms- support vector machine, naïve bayes, decision tree are used. The Naive Slogan The Bayes classification [14] refers to a fundamental probabilistic classification based on strong independent assumptions in the application of the Bayes theorem. The ...

  16. Diabetes Prediction using Machine Learning Algorithms

    This paper focuses on building predictive model using machine learning algorithms and data mining techniques for diabetes prediction. The paper is organized as follows- Section II-gives literature review of the work done on diabetes prediction earlier and taxonomy of machine learning algorithms.

  17. Using Machine Learning for Heart Disease Prediction

    This prediction is an area that is widely researched. Our paper is part of the research on the detection and prediction of heart disease. It is based on the application of Machine Learning ...

  18. Disease Prediction using Machine Learning

    Disease Prediction using Machine Learning Abstract: The dependency on computer-based technology has resulted in storage of lot of electronic data in the health care industry. As a result of which, health professionals and doctors are dealing with demanding situations to research signs and symptoms correctly and perceive illnesses at an early stage.

  19. (PDF) THE PREDICTION OF DISEASE USING MACHINE LEARNING

    Machine Learning is the domain that uses past data for predicting. Machine Learning is the understanding of. computer system under which the Machine Learning model learn from data and experience ...

  20. Enhancing cardiovascular disease prediction: A hybrid machine learning

    This study presents a novel approach to enhance cardiovascular disease prediction using a hybrid machine learning (ML) model. Leveraging on Synthetic Minority oversampling techniques (SMOTE) and adaptive boosting (AdaBoost), we integrate these methods with prominent classifiers, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Extra Tree (ET). Focused on heart rate data ...

  21. Journal of Medical Internet Research

    Background: Acute kidney disease (AKD) affects more than half of critically ill elderly patients with acute kidney injury (AKI), which leads to worse short-term outcomes. Objective: We aimed to establish 2 machine learning models to predict the risk and prognosis of AKD in the elderly and to deploy the models as online apps. Methods: Data on elderly patients with AKI (n=3542) and AKD (n=2661 ...

  22. Chronic kidney disease prediction based on machine learning algorithms

    This approach makes use of a dataset from the UCI Machine Learning Repository 11 referred to as CKD. A total of 24 features and 1 target variable are included in the CKD Dataset. It can be broken down into 2 categories, yes or no. The dataset has 25 attributes, 11 of which are numerical and 14 of which are nominal.

  23. PDF Multiple Disease Prediction System Using Machine Learning

    "Multiple Disease Prediction Using Machine Learning Algorithms" by Chauhan et al. (2021): This paper investigates using various ML algorithms, including SVM and Decision Trees, for multiple disease ... Exploring how to enhance accessibility for healthcare practitioners and ensuring ease of use could be a valuable research focus. 5.

  24. International Classification of Diseases Prediction from MIMIIC-III

    The International Classification of Diseases (ICD) serves as a widely employed framework for assigning diagnosis codes to electronic health records of patients. These codes facilitate the encapsulation of diagnoses and procedures conducted during a patient's hospitalisation. This study aims to devise a predictive model for ICD codes based on the MIMIC-III clinical text dataset. Leveraging ...

  25. Heart Disease Prediction using Machine Learning Techniques

    This research aims to foresee the odds of having heart disease as probable cause of computerized prediction of heart disease that is helpful in the medical field for clinicians and patients [].To accomplish the aim, we have discussed the use of various machine learning algorithms on the data set and dataset analysis is mentioned in this research paper.

  26. Detection of Cardiovascular Diseases from ECG Images Using Machine

    This study leverages deep learning techniques to predict four major cardiac abnormalities—abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal individuals—using a public dataset of ECG images from cardiac patients, and introduces a novel convolutional neural network (CNN) architecture for cardiac abnormality prediction. Abstract: Cardiovascular diseases ...

  27. Early detection of Parkinson's disease using machine learning

    Abstract. Parkinson's disease (PD) is a neurodegenerative disorder affecting 60% of people over the age of 50 years. Patients with Parkinson's (PWP) face mobility challenges and speech difficulties, making physical visits for treatment and monitoring a hurdle. PD can be treated through early detection, thus enabling patients to lead a normal life.

  28. A Comprehensive Study of Deep Learning Methods for Kidney ...

    Kidney disease affects millions worldwide which emphasizes the need for early detection. Recent advancements in deep learning have transformed medical diagnostics and provide promising solutions to detect various kidney diseases. This paper aims to develop a reliable AI based learning system for effective prediction and classification of kidney diseases. The research involves a dataset of ...

  29. [2405.02354] Heterogeneous network and graph attention auto-encoder for

    The emerging research shows that lncRNAs are associated with a series of complex human diseases. However, most of the existing methods have limitations in identifying nonlinear lncRNA-disease associations (LDAs), and it remains a huge challenge to predict new LDAs. Therefore, the accurate identification of LDAs is very important for the warning and treatment of diseases. In this work, multiple ...

  30. Heart Disease Prediction Using Machine Learning

    Cardiovascular disease refers to any critical condition that impacts the heart. Because heart diseases can be life-threatening, researchers are focusing on designing smart systems to accurately diagnose them based on electronic health data, with the aid of machine learning algorithms. This work presents several machine learning approaches for predicting heart diseases, using data of major ...