ECG-based Machine Learning Model Identifies Patients at High Risk for Incident Pulmonary Hypertension
Greg LeeN. HakimRong ChenJochen PfeiferDavid VidmarWebb M. ThompsonArun NemaniR. MiottoKellie MorlandAndrew C. NelsenBrandon K. Fornwalt
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AI can be categorised as either machine learning or deep learning. Machine learning, in essence, is AI that can adjust automatically with little human involvement. Artificial neural networks are used in deep learning, a subclass of machine learning, to simulate the educational process of the human brain. Deep learning is more effective with vast amounts of data than other methods. Traditional machine learning methods, however, do better with smaller amounts of data. In order to train deep learning techniques in a timely manner, a highquality infrastructure is needed. The lengthy training process for a deep learning system is caused by the numerous parameters.It takes two weeks to properly train from scratch the well-known ResNet algorithm. Conventional machine learning algorithms can train in a matter of seconds or hours. The scenario is entirely turned around during the experimentation phase. The deep learning method runs quickly while being tested. When the amount of data increases, the testing time for k-nearest neighbours (a type of machine learning technique) increases. Certain machine learning algorithms also have brief test times, however this is not true of all of them. For many industries to apply other methods utilized in deep learning, interpretation is a major problem.Use this as a case study. Let's say we compute a document's relevance score using deep learning. It delivers very good performance that is comparable to human performance. Nevertheless, there is an issue. The rationale behind that score's award is unknown. Actually, it is mathematically possible to determine which nodes of a sophisticated neural network are active, but we are unsure of the expected appearance of the neurons and the function of these layers of neurons as a whole. As a result, we misinterpret the findings. For machine learning techniques like logistic regression and decision trees, this isn't the actual case. We may directly process photos using DL models, which are displayed as multi-layer chemically synthesized neural networks. The part on data curation covers picture labelling, annotation, data synchronisation, association learning, and segmentation, which is a crucial stage in radiomics and causes interference in non-AI imaging investigations due to variances in imaging procedures. Following that, we devote parts to sample size calculation and various AI techniques. Take into account tests, techniques for enhancing data to deal with limited and unbalanced datasets, and descriptions of Ai techniques (the so-called black box problem). advantages and disadvantages of using ML and DL to implement AI.In a synaptic fashion, applications towards medical imaging are eventually shown. Data science, which also encompasses statistics and predictive modelling, contains deep learning as a key component. Deep learning helps to make this process quicker and simpler for data scientists who are gathering, analysing, and interpreting enormous amounts of data. Simply defined, machine learning enables users to submit huge amounts of information to a computer algorithm, which then SPSS statistics is a multivariate analytics, business intelligence, and criminal investigation data management, advanced analytics, developed by IBM for a statistical software package. A long time, spa inc. Was created by, IBM purchased it in 2009. The brand name for the most recent versions is IBM SPSS statistics. Medical Images, Deep Feature Extraction, Predictive Modelling and Prediction. The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .860which indicates 86% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis. Emotional Intelligence the Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .860which indicates 86% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.
Instance-based learning
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Word2vec
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Abstract Image classification of maize disease is an agriculture computer vision application. In general, the application of computer vision uses two methods: machine learning and deep learning. Implementations of machine learning classification cannot stand alone. It needs image processing techniques such as preprocessing, feature extraction, and segmentation. Usually, the features are selected manually. The classification uses k-nearest neighbor, naïve bayes, decision tree, random forest, and support vector machine. On the other side, deep learning is part of machine learning. It is a development of an artificial neural network that performs automatic feature extraction. Deep learning is capable of recognizing large data but requires high-speed computation. This article compare machine learning and deep learning for maize leaf disease classification. There are five research questions: how to get data, how machine learning and deep learning classify images, how the classification result compare both of them and the opportunities & challenges of research on maize leaf disease classification. The number of articles to review was 62, consisting of 18 articles using machine learning, 28 articles applying deep learning, and the rest are supporting articles.
Contextual image classification
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ABSTRACT Background It is important to be able to predict, for each individual patient, the likelihood of later metastatic occurrence, because the prediction can guide treatment plans tailored to a specific patient to prevent metastasis and to help avoid under- or over-treatment. Deep Neural Network (DNN) learning, commonly referred to as deep learning, has become popular due to its success in image detection and prediction, but questions such as whether deep learning outperforms other machine learning methods when using non-image clinical data remain unanswered. Grid search has been introduced to deep learning hyperparameter tunning for the purpose of improving its prediction performance, but the effect of grid search on other machine learning methods are under-studied. In this research, we take the empirical approach to study the performance of deep learning and other machine learning methods when using non-image clinical data to predict the occurrence of breast cancer metastasis (BCM) 5, 10, or 15-years after the initial treatment. We developed DNN models as well as models using 9 other machine learning methods including Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), LASSO, Decision Tree (DT), k-Nearest Neighbors (KNN), Random Forrest (RF), AdaBoost (ADB), and XGBoost (XGB). We used grid search to tune hyperparameters for all methods. We then compared the deep learning models to the models trained using the 9 other machine learning methods. Results Based on the mean test AUC results, DNN ranks 6th, 4th, and 3rd when predicting 5-year, 10-year, and 15-year BCM respectively, out of 10 machine learning methods. The top performing methods in predicting 5-year BCM are XGB(1st), RF(2nd), and KNN(3rd). For predicting 10-year BCM the top performers are XGB (1st), RF(2nd), and NB(3rd) . Finally, for 15-year BCM the top performers are SVM (1st), LR and LASSO (tied for 2nd), and DNN (3rd). The ensemble methods RF and XGB outperform other methods when data are less balanced, while SVM, LR, LASSO, and DNN outperform other methods when data are more balanced. Our statistical testing results show that at a significance level of 0.05 DNN overall performs no worse than other machine learning methods when predicting 5-year, 10-year, and 15-year BCM. Conclusions Our results show that deep learning with grid search overall performs at least as well as other machine learning methods when using non-image clinical data. It is interesting to note that some of the other machine learning methods such as XGB, RF, and SVM are very strong competitors of DNN when incorporating grid search. It is also worth noting that the computation time required to do grid search with DNN is way more than that required to do grid search with the other 9 machine learning methods.
Hyperparameter Optimization
Hyperparameter
AdaBoost
Lasso
Ensemble Learning
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The creation of an intelligent system that works like a human is due to Artificial intelligence (AI). It can be broadly classified into four techniques: machine learning, machine vision, automation and Robotics and natural language processing. These domains can learn from data provided, identify the hidden pattern and make decisions with human intervention. There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Thus, to reduce the risk factor while decision making, machine learning techniques are more beneficial. The benefit of machine learning is that it can do the work automatically, once it learns what to do. Therefore, in this work, we discuss the theory behind machine learning techniques and the tasks they perform such as classification, regression, clustering, etc. We also provide a review of the state of the art of several machine learning algorithms like Naive Bayes, random forest, K-Means, SVM, etc., in detail.
Instance-based learning
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Learning classifier system
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The advancement in Artificial Intelligence has led to the improvement in human lives. Machine learning algorithms in particular and Artificial Intelligence in general have become very useful in today's activities. One of the sectors that has benefited from the new technology is the health sector. Machine learning techniques have been useful in the diagnosis and prediction of rare diseases. Many health sectors are using the techniques for the diagnosis and prediction of diseases thereby improving on the health situations of the patients in record time. Artificial Intelligence-based methods help in reducing the doctor to patients' ratio gap by providing machine learning alternatives in the prediction of diseases. In this paper, we give an overview of different machine learning techniques and their relevance in the diagnosis of particular diseases. Machine learning algorithms such as Support Vector Machine (SVM), Naïve Bayes, K Nearest Neighbor (KNN),Decision Tree (DT),Random Forest (RF), Artificial neural network (ANN), Convolution Neural Network (CNN), Logistic Regression and Linear Regression used for the diagnosis of diseases have been reviewed. A collection of most non-communicable diseases diagnosed using machine learning has been examined. A comparative analysis of the accuracy performance to diagnose diseases with different machine learning algorithms has also been presented.
Relevance vector machine
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Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and an understanding of its application prospect in animal diseases.
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Supervised Learning
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Machine Learning (ML) is a technology that can revolutionize the world. It is a technology based on AI (Artificial Intelligence) and can predict the outcomes using the previous algorithms without programming it. A subset of artificial intelligence is called machine learning (AI). A machine may automatically learn from data and get better at what it does thanks to machine learning. “If additional data can be gathered to help a machine perform better, it can learn. A developing technology called machine learning allows computers to learn from historical data. Machines can predict the outcomes by machine learning. For Nowadays machine learning is very important for us because it makes our work easy. to many companies are using machine learning in their products, like google is using google its google assistant, which takes our voice command and gives what do we want from it, and google is also using its goggle lens form which we can find anything just by clicking a picture, and Netflix is using machine learning for recommendation of any movies or series, Machine learning has a very deep effect on our life, like nowadays we are using selfdriving car’s.
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Machine learning is often perceived as a sophisticated technology accessible only by highly trained experts. This prevents many physicians and biologists from using this tool in their research. The goal of this paper is to eliminate this out-dated perception. We argue that the recent development of auto machine learning techniques enables biomedical researchers to quickly build competitive machine learning classifiers without requiring in-depth knowledge about the underlying algorithms. We study the case of predicting the risk of cardiovascular diseases. To support our claim, we compare auto machine learning techniques against a graduate student using several important metrics, including the total amounts of time required for building machine learning models and the final classification accuracies on unseen test datasets. In particular, the graduate student manually builds multiple machine learning classifiers and tunes their parameters for one month using scikit-learn library, which is a popular machine learning library to obtain ones that perform best on two given, publicly available datasets. We run an auto machine learning library called auto-sklearn on the same datasets. Our experiments find that automatic machine learning takes 1 h to produce classifiers that perform better than the ones built by the graduate student in one month. More importantly, building this classifier only requires a few lines of standard code. Our findings are expected to change the way physicians see machine learning and encourage wide adoption of Artificial Intelligence (AI) techniques in clinical domains.
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