scholarly journals Depression analysis of voice samples using machine learning

2021 ◽  
Vol 23 (11) ◽  
pp. 429-438
Author(s):  
Yashi Sharma ◽  
◽  
Dr. Brajesh Kumar Singh ◽  
Keyword(s):  
Machine Learning ◽  
Large Data ◽  
World Health ◽  
Medical Attention ◽  
Data Set ◽  
Symptoms Of Depression ◽  
Medical Response ◽  
Lifestyle Pattern ◽  
Health Organization ◽  
The Masses

Depression is seen as an emerging mental challenge in the lives of various people. Nowadays it is also becoming one of the major reasons for mental disability across the world. Depression has manifested itself as a silent killer and according to statistics it has affected more than 300 million people in United States of America majorly affecting individuals in the age group of 15 to 44 yrs. According to a study by World Health Organization, the effects of depression have been dangerous in life, it is seen causing threatening diseases like cancer, diabetic issues or even heart disease. However, the problem that mainly is associated with the disease of depression is that it is not treated as a disease. Where the common understanding of the word “Disease” is any medical ailment that require doctor’s attention or quick medical response, depression on the other hand, even after qualifying as a disease is hidden in societal barriers to appear for a proper treatment. People whose lifestyle pattern has been intruded by depression either do not avail proper medical attention or are too shy to appear in the masses for proper attention on their physical as well as condition. Our motivation here is to investigate through the phenomenon of depression and predict whether an individual is having symptoms of depression by accessing his/her voice sample. In order to establish a link between depression and voice features, we obtain a large data set and then train a model accordingly by applying machine learning methods on it. This model when given a voice sample can now predict, whether a particular subject is depressed or not, to a nearby accurate measure.

scholarly journals Correlation Between Temperature and COVID-19 (Suspected, Confirmed and Death) Cases based on Machine Learning Analysis

2020 ◽  
Vol 14 (suppl 1) ◽  
pp. 1017-1024 ◽  
Author(s):  
Mohammad Khubeb Siddiqui ◽  
Ruben Morales-Menendez ◽  
Pradeep Kumar Gupta ◽  
Hafiz M.N. Iqbal ◽  
Fida Hussain ◽  
...  
Keyword(s):  
Machine Learning ◽  
World Health ◽  
Effect Of Temperature ◽  
Machine Learning Method ◽  
Contributing Factors ◽  
Data Set ◽  
The World ◽  
Health Organization ◽  
Death Cases ◽  
Learning Analysis

Currently, the whole world is struggling with the biggest health problem COVID-19 name coined by the World Health Organization (WHO). This was raised from China in December 2019. This pandemic is going to change the world. Due to its communicable nature, it is contagious to both medically and economically. Though different contributing factors are not known yet. Herein, an effort has been made to find the correlation between temperature and different cases situation (suspected, confirmed, and death cases). For a said purpose, k-means clustering-based machine learning method has been employed on the data set from different regions of China, which has been obtained from the WHO. The novelty of this work is that we have included the temperature field in the original WHO data set and further explore the trends. The trends show the effect of temperature on each region in three different perspectives of COVID-19 – suspected, confirmed and death.

scholarly journals Influence of important environmental parameters on the spread and severity of COVID-19: Part 1

2020 ◽  
pp. 1-12
Author(s):  
Vikrant Tiwari ◽  
Nimisha Sharma
Keyword(s):  
Large Scale ◽  
Environmental Parameters ◽  
Large Data ◽  
Epidemiological Data ◽  
Good Health ◽  
World Health ◽  
Data Set ◽  
Contagious Diseases ◽  
Health Organization ◽  
High Degree

In the absence of the detailed COVID-19 epidemiological data or large benchmark studies, an effort has been made to explore and correlate the relation of parameters like environment, economic indicators, and the large scale exposure of different prevalent diseases, with COVID-19 spread and severity amongst the different countries affected by COVID-19. Data for environmental, socio-economic and others important infectious diseases were collected from reliable and open source resources like World Health Organization, World Bank, etc. Further, this large data set is utilized to understand the COVID-19 worldwide spread using simple statistical tools. Important observations that are made in this study are the high degree of resemblance in the pattern of temperature and humidity distribution among the cities severely affected by COVID-19. Further, It is surprising to see that in spite of the presence of many environmental parameters that are considered favorable (like clean air, clean water, EPI, etc.), many countries are suffering with the severe consequences of this disease. Lastly a noticeable segregation among the locations affected by different prevalent diseases (like Malaria, HIV, Tuberculosis, and Cholera) was also observed. Among the considered environmental factors, temperature, humidity and EPI should be an important parameter in understanding and modelling COVID-19 spreads. Further, contrary to intuition, countries with strong economies, good health infrastructure and cleaner environment suffered disproportionately higher with the severity of this disease. Therefore, policymaker should sincerely review their country preparedness toward the potential future contagious diseases, weather natural or manmade.

scholarly journals Cloud Based Predictive Model for Airborne Disease Based Healthcare Data

2019 ◽  
Vol 9 (2) ◽  
pp. 837-840
Keyword(s):  
Machine Learning ◽  
Infectious Diseases ◽  
Predictive Model ◽  
Health Impact ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Medical Attention ◽  
Healthcare Data ◽  
Health Organization ◽  
Algorithmic Approaches

Nowadays, the airborne particles have major health impact when it spreads in human, plant and animal beings. Infectious diseases spreads from these particles which are exhaled directly into the air through the exertions of coughing, breathing, talking and sneezing etc. According to the report from World Health Organization (WHO), More than 30 infectious diseases have arrived to harm the health of people in the past years. There’s no medical attention for several infectious diseases to take prevention and remedy. India have lack of healthcare data to take control of the endemic infectious diseases. This paper uses predictive model which is provide a preventive guidance and suggestions for predicted Airborne diseases through machine learning algorithms. Azure machine learning studio is a cloud based environment which provides machine learning algorithmic approaches to make an intelligent model based solution to solve the particular domain based problems. This proposed model will produce an efficient outcome and helps to take better protection from the infectious diseases.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

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.

Risk Factor Identification, Classification and Prediction Summary of Chronic Kidney Disease

2020 ◽  
Vol 13 ◽  
Author(s):  
Pramila Arulanthu ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Disease Diagnosis ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Medical Field ◽  
Data Set ◽  
Medical Issues ◽  
Health Organization

: The medical data has an enormous quantity of information. This data set requires effective classification for accurate prediction. Predicting medical issues is an extremely difficult task in which Chronic Kidney Disease (CKD) is one of the major unpredictable diseases in medical field. Perhaps certain medical experts do not have identical awareness and skill to solve the issues of their patients. Most of the medical experts may have underprivileged results on disease diagnosis of their patients. Sometimes patients may lose their life in nature. As per the Global Burden of Disease (GBD-2015) study, death by CKD was ranked 17th place and GBD-2010 report 27th among the causes of death globally. Death by CKD is constituted 2·9% of all death between the year 2010 and 2013 among people from 15 to 69 age. As per World Health Organization (WHO-2005) report, 58 million people expired by CKD. Hence, this article presents the state of art review on Chronic Kidney Disease (CKD) classification and prediction. Normally, advanced data mining techniques, fuzzy and machine learning algorithms are used to classify medical data and disease diagnosis. This study reviews and summarizes many classification techniques and disease diagnosis methods presented earlier. The main intention of this review is to point out and address some of the issues and complications of the existing methods. It is also attempts to discuss the limitations and accuracy level of the existing CKD classification and disease diagnosis methods.

scholarly journals Generation of geometric interpolations of building types with deep variational autoencoders

2020 ◽  
Vol 6 ◽  
Author(s):  
Jaime de Miguel Rodríguez ◽  
Maria Eugenia Villafañe ◽  
Luka Piškorec ◽  
Fernando Sancho Caparrini
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Large Data ◽  
Learning Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Connectivity Map ◽  
Data Set ◽  
3D Objects ◽  
Machine Learning Model

Abstract This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

Health Assessment and Screening During Adolescence

PEDIATRICS ◽  
1987 ◽  
Vol 80 (1) ◽  
pp. 130-1-158
Author(s):  
Andrea Marks ◽  
Martin Fisher
Keyword(s):  
Health Professional ◽  
Health Assessment ◽  
Health Screening ◽  
Medical Professional ◽  
World Health ◽  
Medical Attention ◽  
Optimal Time ◽  
Medical Checkup ◽  
American Tradition ◽  
Health Organization

The "medical checkup," like hot dogs and apple pie, has become an American tradition. Adolescents have checkups requested by schools, summer camps, sports teams, employers, parents, and, less frequently, themselves. At such times, a cursory chat between the teenager and health professional, followed by a quick physical examination, is unlikely to detect the most prevalent and significant health problems of young people today. The very nature of such an interaction may even serve to alienate the teenager from the health care system or provider. Alternatively, if properly focused and thorough, a checkup may not only uncover important areas of disease or dysfunction, but also should initiate a meaningful dialogue and relationship between the adolescent patient and health professional. A checkup generally occurs when an individual feels well and visits a medical professional without complaint or a checkup may be had in conjunction with medical attention to a specific problem. In either case, the primary purpose of a checkup is health assessment and screening. Health screening has been defined by the World Health Organization as ". . . the presumptive identification of unrecognized disease or defect by the application of tests, examinations, or other procedures which can be applied rapidly." The purpose of health screening is to detect a problem (or problems) before it would usually become apparent or before medical attention is sought, with the intent of initiating treatment at an earlier and more optimal time, so as to prevent or favorably alter its course and consequences. Screening is not in itself diagnostic.

Comparativa de modelos multivariados basados en aprendizaje automático para la predicción del riesgo de diabetes en etapas tempranas

2021 ◽  
Author(s):  
◽  
Zayra Ramírez Gaytán
Keyword(s):  
World Health ◽  
Degenerative Diseases ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
The World ◽  
Long Time ◽  
Life Threatening ◽  
Health Organization ◽  
Logistic Regression Algorithm ◽  
Asymptomatic Phase

Diabetes is one of the fastest-growing, life-threatening, chronic degenerative diseases. According to the World Health Organization (WHO), it has affected 422 million people worldwide in 2018. Approximately 50% of all people who suffer diabetes are not diagnosed due to the asymptomatic phase which usually lasts a long time. In this work, a data set of 520 instances has been used. The data set has been analyzed with the next three algorithms: logistic regression algorithm, decision trees and random forest. The results show that the decision tree algorithm had better performance with an AUC of 98%. Also, it was found the most common symptoms that a person with a risk of diabetes presents are polyuria, polydipsia and sudden weight loss.

scholarly journals A Comparison on Supervised and Semi-Supervised Machine Learning Classifiers for Gestational Diabetes Prediction

2021 ◽  
Vol 9 (12) ◽  
pp. 1001-1005
Author(s):  
Lokesh Kola
Keyword(s):  
Machine Learning ◽  
Gestational Diabetes ◽  
Supervised Learning ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
World Health ◽  
Middle Income ◽  
Diabetes Prediction ◽  
Supervised Machine Learning Classifiers ◽  
Health Organization

Abstract: Diabetes is the deadliest chronic diseases in the world. According to World Health Organization (WHO) around 422 million people are currently suffering from diabetes, particularly in low and middle-income countries. Also, the number of deaths due to diabetes is close to 1.6 million. Recent research has proven that the occurrence of diabetes is likely to be seen in people aged between 18 and this has risen from 4.7 to 8.5% from 1980 to 2014. Early diagnosis is necessary so that the disease does not go into advanced stages which is quite difficult to cure. Significant research has been performed in diabetes predictions. As time passes, challenges keep increasing to build a system to detect diabetes systematically. The hype for Machine Learning is increasing day to day to analyse medical data to diagnose a disease. Previous research has focused on just identifying the diabetes without specifying its type. In this paper, we have we have predicted gestational diabetes (Type-3) by comparing various supervised and semi-supervised machine learning algorithms on two datasets i.e., binned and non-binned datasets and compared the performance based on evaluation metrics. Keywords: Gestational diabetes, Machine Learning, Supervised Learning, Semi-Supervised Learning, Diabetes Prediction

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