scholarly journals Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques

2020 ◽  
Author(s):  
Akshay Kumar ◽  
Farhan Mohammad Khan ◽  
Rajiv Gupta ◽  
Harish Puppala
Keyword(s):  
Machine Learning ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Learning Tools ◽  
Learning Techniques ◽  
Health Organization ◽  
Respiratory Coronavirus ◽  
Criticality Index

AbstractThe outbreak of COVID-19 is first identified in China, which later spread to various parts of the globe and was pronounced pandemic by the World Health Organization (WHO). The disease of transmissible person-to-person pneumonia caused by the extreme acute respiratory coronavirus 2 syndrome (SARS-COV-2, also known as COVID-19), has sparked a global warning. Thermal screening, quarantining, and later lockdown were methods employed by various nations to contain the spread of the virus. Though exercising various possible plans to contain the spread help in mitigating the effect of COVID-19, projecting the rise and preparing to face the crisis would help in minimizing the effect. In the scenario, this study attempts to use Machine Learning tools to forecast the possible rise in the number of cases by considering the data of daily new cases. To capture the uncertainty, three different techniques: (i) Decision Tree algorithm, (ii) Support Vector Machine algorithm, and (iii) Gaussian process regression are used to project the data and capture the possible deviation. Based on the projection of new cases, recovered cases, deceased cases, medical facilities, population density, number of tests conducted, and facilities of services, are considered to define the criticality index (CI). CI is used to classify all the districts of the country in the regions of high risk, low risk, and moderate risk. An online dashpot is created, which updates the data on daily bases for the next four weeks. The prospective suggestions of this study would aid in planning the strategies to apply the lockdown/ any other plan for any country, which can take other parameters to define the CI.

scholarly journals Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

2018 ◽  
Author(s):  
Sandip S Panesar ◽  
Rhett N D’Souza ◽  
Fang-Cheng Yeh ◽  
Juan C Fernandez-Miranda
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Molecular Characteristics ◽  
Regression Methods ◽  
Learning Techniques ◽  
The World ◽  
Health Organization

AbstractBackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.MethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).ResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.ConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions (Preprint)

2018 ◽  
Author(s):  
Roberto Acuña
Keyword(s):  
Machine Learning ◽  
Suicidal Ideation ◽  
Suicide Ideation ◽  
Machine Learning Techniques ◽  
World Health ◽  
Global Public Health ◽  
Systematic Analysis ◽  
Middle Income ◽  
Learning Techniques ◽  
Health Organization

BACKGROUND According to the World Health Organization (WHO) close to 800,000 people worldwide death by suicidal each year. Many more attempt to do it. In consequence, the WHO recognizes suicide as a global public health priority, which affects not only rich countries, but poor and middle income countries as well. OBJECTIVE The aim of this study is to evaluate several supervised classifiers for detecting messages with suicidal ideation in order to know if these systems can be used in automatic suicide prevention systems. METHODS We used machine learning techniques to make a systematic analysis of 28 supervised classifier algorithms with parameters by defect. The Life Corpus, used in this research, is a bilingual corpus (English and Spanish) oriented to suicide. The corpus was constructed by two annotation experts, retrieving texts from several social networks. The corpus quality was measured using mutual annotation agreement. RESULTS The different experiments determined that the classifier with the best performance was KStar, with the corpus version POS-SYNSETS-NUM; and the cycle with 2 classes Urgent and No Risk was the one that achieved the best results with the PRC-Area metrics of 0,81036 and F-measure of 0,7148. CONCLUSIONS The present research fulfilled the objective of discovering which characteristics are the most suitable for the automatic classification of messages with suicidal ideation, using the Life Corpus. The results of this evaluation demonstrate that the Life Corpus and machine learning techniques could be suitable for detecting suicide ideation messages.

scholarly journals An Efficient Model for Predicting Student Dropout using Data Mining and Machine Learning Techniques

2019 ◽  
Vol 8 (9S2) ◽  
pp. 750-752
Keyword(s):  
Machine Learning ◽  
Similarity Measures ◽  
Machine Learning Techniques ◽  
World Health ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Student Dropout ◽  
Learning Techniques ◽  
Using Data ◽  
Health Organization

Education could be a important resource that has to lean to all or any kids. one in all the largest assets of the longer term generation cloud is alleged because the education that's given to the youngsters. Most of the youngsters aren't ready to continue their education because of many reasons. The prediction of student dropout plays a very important role in characteristic the scholars World Health Organization are on the sting of being a dropout from their education. whereas predicting this, we will simply try and solve their issues and create them continue their education. during this paper, we've planned a model for predicting the scholars can get born out or not mistreatment many machine learning techniques. we have a tendency to create use of decision trees that make a call mistreatment many factors. the choice of the prediction involves crucial wherever many knowledge attributes are used for prediction like correlations, similarity measures, frequent patterns, and associations rule mining. The planned work is evaluated mistreatment numerous parameters and is well-tried to figure expeditiously in predicting the dropout students compared with alternative.

Machine Learning Based Coronary Artery Disease Prediction

2020 ◽  
Vol 17 (9) ◽  
pp. 3999-4002
Author(s):  
A. C. Bhavani ◽  
K. Aditya Shastry ◽  
K. Deepika ◽  
Nithya N. Shanbag ◽  
G. C. Akshatha
Keyword(s):  
Machine Learning ◽  
Coronary Artery ◽  
Medical Diagnosis ◽  
Performance Metrics ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Health Organization

The world health organization (WHO) has assessed that the death of around 12 million people across the globe is observed each year because of diseases related to cardiovascular. The dangers associated with the cardiovascular disease can be identified effectively using machine learning techniques. As per survey, around 30% of the patient suffers no symptoms during heart attacks. But the bloodstream contains unique indications of the attack for days. The medical diagnosis of a patient remains a complex task due to several factors. The accurate medical diagnosis of a patient’s heart disease is critical as it significantly leads to the saving of millions of human lives. In this regard, the automation of the medical diagnosis is significant. The goal of this work is the development of a system for predicting the disease related to coronary artery in a patient with high accuracy utilizing machine learning (ML) techniques. Several algorithms like Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) classifiers were implemented for predicting the disease. Extensive experiments demonstrated that the naïve Bayes achieved higher accuracy than the DT and SVM with regards to accuracy, precision, F-Measure, Recall, and receiver operating characteristic (ROC) performance metrics.

scholarly journals Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 589 ◽  
Author(s):  
Franco Valencia ◽  
Alfonso Gómez-Espinosa ◽  
Benjamín Valdés-Aguirre
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Emerging Market ◽  
Data Availability ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Tools ◽  
Price Movement ◽  
High Data ◽  
Learning Techniques

Cryptocurrencies are becoming increasingly relevant in the financial world and can be considered as an emerging market. The low barrier of entry and high data availability of the cryptocurrency market makes it an excellent subject of study, from which it is possible to derive insights into the behavior of markets through the application of sentiment analysis and machine learning techniques for the challenging task of stock market prediction. While there have been some previous studies, most of them have focused exclusively on the behavior of Bitcoin. In this paper, we propose the usage of common machine learning tools and available social media data for predicting the price movement of the Bitcoin, Ethereum, Ripple and Litecoin cryptocurrency market movements. We compare the utilization of neural networks (NN), support vector machines (SVM) and random forest (RF) while using elements from Twitter and market data as input features. The results show that it is possible to predict cryptocurrency markets using machine learning and sentiment analysis, where Twitter data by itself could be used to predict certain cryptocurrencies and that NN outperform the other models.

scholarly journals Efficient Machine Learning Techniques to Detect Glaucoma using Structure and Texture based Features

2020 ◽  
Vol 9 (2) ◽  
pp. 193-201
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Texture Features ◽  
Structural Features ◽  
Machine Learning Techniques ◽  
World Health ◽  
Fundus Image ◽  
Glaucoma Detection ◽  
Learning Techniques ◽  
Health Organization

Survey of world health organization has revealed that retinal eye disease Glaucoma is the second leading cause for the blindness worldwide. It is the disease which will steal the vision of the patient without any warning or symptoms. About half of the world Glaucoma patients are estimated to be in Asia. Hence, for social and economic reasons, Glaucoma detection is necessary in preventing blindness and reducing the cost of surgical treatment of the disease. The objective of the paper is to predict and detect Glaucoma efficiently using image processing and machine learning based classification techniques. Segmentation techniques such as unique template approach, Gray Level Coherence Matrix based feature extraction approach and wavelet transform based approach are used to extract these structure and texture based features. Combination of structure based and texture based techniques along with machine learning techniques improves the efficiency of the system. Developed efficient Computer aided Glaucoma detection system classifies a fundus image as either Normal or Glaucomatous image based on the structural features of the fundus image such as Cup-to-Disc Ratio (CDR), Rim-to-Disc Ratio (RDR), Superior and Inferior neuro-retinal rim thicknesses, Vessel structure based features and Distribution of texture features in the fundus images.

scholarly journals Survival prediction among heart patients using machine learning techniques

2021 ◽  
Vol 19 (1) ◽  
pp. 134-145
Author(s):  
Abdulwahab Ali Almazroi ◽  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Survival Prediction ◽  
Learning Techniques ◽  
Artificial Neural

<abstract><p>Cardiovascular diseases are regarded as the most common reason for worldwide deaths. As per World Health Organization, nearly $ 17.9 $ million people die of heart-related diseases each year. The high shares of cardiovascular-related diseases in total worldwide deaths motivated researchers to focus on ways to reduce the numbers. In this regard, several works focused on the development of machine learning techniques/algorithms for early detection, diagnosis, and subsequent treatment of cardiovascular-related diseases. These works focused on a variety of issues such as finding important features to effectively predict the occurrence of heart-related diseases to calculate the survival probability. This research contributes to the body of literature by selecting a standard well defined, and well-curated dataset as well as a set of standard benchmark algorithms to independently verify their performance based on a set of different performance evaluation metrics. From our experimental evaluation, it was observed that decision tree is the best performing algorithm in comparison to logistic regression, support vector machines, and artificial neural networks. Decision trees achieved $ 14 $% better accuracy than the average performance of the remaining techniques. In contrast to other studies, this research observed that artificial neural networks are not as competitive as the decision tree or support vector machine.</p></abstract>

scholarly journals An Interactive Health Game Using Machine Learning: A Prototype

2020 ◽  
Author(s):  
Esra Ay ◽  
Burak Eken ◽  
Tuğba Önal-Süzek
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Real Life ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
World Health ◽  
Applied Machine Learning ◽  
The Metabolic Syndrome ◽  
Learning Techniques ◽  
Health Organization

AbstractAccording to World Health Organization (WHO) 2016 report, there are over 650 million obese adults and more than 2 billion overweight individuals in the world and it is estimated that this number will reach 2.7 billion in 2025 [1]. A sedentary lifestyle with low physical activity is considered to be one of the most effective environmental effects leading to various chronic disease phenotypes such as obesity and metabolic syndrome. On average, every 1 out of 3 people over the age of 20 in Turkey are known to have struggled with the metabolic syndrome [2]. Our project aims to apply the concept of “serious gaming”, to entertain people, play games, socialize and exercise in parallel to increase the ratio of the healthy individuals in our society. In this project, we applied machine learning techniques to integrate real-life accelerometer and gyroscope sensor data obtained from mobile phones to develop an interactive mobile based exercise game which does not require any external device such as smart watches. To our knowledge and research, our game is the first mobile-only interactive serious game that integrates machine learning techniques and an encouraging virtual environment to the individuals in need of exercise.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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