The use of multimedia medical data and machine learning for various diagnoses

2018 ◽  
Vol 165 ◽  
pp. A1 ◽  
Author(s):  
Chung-Ming Lo ◽  
Yu-Chuan (Jack) Li
Keyword(s):  
Machine Learning ◽  
Medical Data

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

2021 ◽  
Author(s):  
Roohallah Alizadehsani ◽  
Mohamad Roshanzamir ◽  
Sadiq Hussain ◽  
Abbas Khosravi ◽  
Afsaneh Koohestani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Probability Theory ◽  
Medical Data

scholarly journals Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

2020 ◽  
Vol 9 (5) ◽  
pp. 288-297
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
High Performance ◽  
Predictive Modelling ◽  
Medical Application ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Screening Process ◽  
Huge Data ◽  
System Data

Predictive modelling is a mathematical technique which uses Statistics for prediction, due to the rapid growth of data over the cloud system, data mining plays a significant role. Here, the term data mining is a way of extracting knowledge from huge data sources where it’s increasing the attention in the field of medical application. Specifically, to analyse and extract the knowledge from both known and unknown patterns for effective medical diagnosis, treatment, management, prognosis, monitoring and screening process. But the historical medical data might include noisy, missing, inconsistent, imbalanced and high dimensional data.. This kind of data inconvenience lead to severe bias in predictive modelling and decreased the data mining approach performances. The various pre-processing and machine learning methods and models such as Supervised Learning, Unsupervised Learning and Reinforcement Learning in recent literature has been proposed. Hence the present research focuses on review and analyses the various model, algorithm and machine learning technique for clinical predictive modelling to obtain high performance results from numerous medical data which relates to the patients of multiple diseases.

scholarly journals System to assist in the Diagnosis of Diabetes using Ontology and Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 9971-9975
Keyword(s):  
Machine Learning ◽  
Public Health Problem ◽  
Medical Data ◽  
The Body ◽  
Machine Learning Method ◽  
Learning Method ◽  
Automatic Learning ◽  
Early Diabetes ◽  
Data Set ◽  
Medical Tests

Diabetes mellitus has become a public health problem in both developed and developing countries. If it is not treated early, diabetes-related complications in many vital organs of the body can become fatal. Its early detection is very important for early treatment that can prevent the disease from progressing to such complications. This article focuses on designing a system to assist in the diagnosis of diabetes disease based on medical ontology and automatic learning. The proposed method uses automatic learning algorithms as a classifier for the diagnosis of diabetes based on a medical data set. The ontology suggests a pre-processing of a coherent, consistent, interoperable and shareable knowledge basis of data and the machine learning method focuses on classification based on symptoms and medical tests. Based on the experimental results, DDAS not only offers better performance in predicting and diagnosing diabetes in individuals, but also has better accuracy in recommending useful treatment to patients.

scholarly journals Determination of Significant Features for Building an Efficient Heart Disease Prediction System

2019 ◽  
Vol 8 (2) ◽  
pp. 4499-4504
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Heart Diseases ◽  
Medical Diagnostics ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Prediction System ◽  
Early Stages

Heart diseases are responsible for the greatest number of deaths all over the world. These diseases are usually not detected in early stages as the cost of medical diagnostics is not affordable by a majority of the people. Research has shown that machine learning methods have a great capability to extract valuable information from the medical data. This information is used to build the prediction models which provide cost effective technological aid for a medical practitioner to detect the heart disease in early stages. However, the presence of some irrelevant and redundant features in medical data deteriorates the competence of the prediction system. This research was aimed to improve the accuracy of the existing methods by removing such features. In this study, brute force-based algorithm of feature selection was used to determine relevant significant features. After experimenting rigorously with 7528 possible combinations of features and 5 machine learning algorithms, 8 important features were identified. A prediction model was developed using these significant features. Accuracy of this model is experimentally calculated to be 86.4%which is higher than the results of existing studies. The prediction model proposed in this study shall help in predicting heart disease efficiently.

Machine Learning-Based Approach for Predictive Analytics in Healthcare

2022 ◽  
pp. 182-206
Author(s):  
Sandeep Kumar Hegde ◽  
Monica R. Mundada
Keyword(s):  
Machine Learning ◽  
Mortality Rate ◽  
Chronic Diseases ◽  
Domain Knowledge ◽  
Predictive Analytics ◽  
Future Trend ◽  
Medical Data ◽  
World Health ◽  
Premature Deaths ◽  
Health Organization

In this internet era, due to digitization in every application, a huge amount of data is produced digitally from the healthcare sectors. As per the World Health Organization (WHO), the mortality rate due to the various chronic diseases is increasing each day. Every year these diseases are taking lives of at least 50 million people globally, which includes even premature deaths. These days, machine learning (ML)-based predictive analytics are turning out as effective tools in the healthcare sectors. These techniques can extract meaningful insights from the medical data to analyze the future trend. By predicting the risk of diseases at the preliminary stage, the mortality rate can be reduced, and at the same time, the expensive healthcare cost can be eliminated. The chapter aims to briefly provide the domain knowledge on chronic diseases, the biological correlation between theses disease, and more importantly, to explain the application of ML algorithm-based predictive analytics in the healthcare sectors for the early prediction of chronic diseases.

scholarly journals Machine learning based outlier detection for medical data

2021 ◽  
Vol 24 (1) ◽  
pp. 564
Author(s):  
R. Vijaya Kumar Reddy ◽  
Shaik Subhani ◽  
B. Srinivasa Rao ◽  
N. Lakshmipathi Anantha
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Outlier Detection ◽  
Health Care Industry ◽  
Work Load ◽  
Medical Data ◽  
The Novel ◽  
Cleaning Process ◽  
Care Industry ◽  
Global Data

<p>The concept of machine learning generate best results in health care data, it also reduce the work load of health care industry. This algorithm potentially overcome the issues and find out the novel knowledge for development of medical date in health care industry. In this paper propose a new algorithm for finding the outliers using different datasets. Considering that medical data are analytic of mutually health problems and an activity. The proposed algorithm is working based on supervised and unsupervised learning. This algorithm detects the outliers in medical data. The effectiveness of local and global data factor for outlier detection for medical data in real time. Whatever, the model used in this scenario from their training and testing of medical data. The cleaning process based on the complete attributes of dataset of similarity operations. Experiments are conducted in built in various medical datasets. The statistical outcome describe that the machine learning based outlier finding algorithm given that best accurateness.</p>

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