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.

Applying Bayesian Networks in the Early Diagnosis of Bulimia and Anorexia Nervosa in Adolescents

2018 ◽  
pp. 364-379 ◽  
Author(s):  
Placido Rogerio Pinheiro ◽  
Mirian Caliope Dantas Pinheiro ◽  
Victor Câmera Damasceno ◽  
Marley Costa Marques ◽  
Raquel Souza Bino Araújo ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Early Diagnosis ◽  
Chronic Diseases ◽  
World Health ◽  
Unified Health System ◽  
Premature Deaths ◽  
Health Organization ◽  
And Control ◽  
Nursing Diagnoses

The diseases and health problems are concerns of managers of the Unified Health System has costs in more sophisticated care sector are high. The World Health Organization focused on prevention of chronic diseases to prevent millions of premature deaths in the coming years, bringing substantial gains in economic growth by improving the quality of life. Few countries appear to be aimed at prevention, if not note the available knowledge and control of chronic diseases and may represent an unnecessary risk to future generations. Early diagnosis of these diseases is the first step to successful treatment in any age group. The objective is to build a model, from the establishment of a Bayesian network, for the early diagnosis of nursing to identify eating disorders bulimia and anorexia nervosa in adolescents, from the characteristics of the DSM-IV and Nursing Diagnoses The need for greater investment in technology in public health actions aims to increase the knowledge of health professionals, especially nurses, contributing to prevention, decision making and early treatment of problems.

scholarly journals Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model

2021 ◽  
Vol 17 (08) ◽  
pp. 154
Author(s):  
Shakir Khan
Keyword(s):  
Machine Learning ◽  
Saudi Arabia ◽  
Prediction Models ◽  
Preventive Measures ◽  
Future Trend ◽  
Control Measures ◽  
World Health ◽  
Seir Model ◽  
The World ◽  
Health Organization

<p>The World Health Organization (WHO) reported the COVID-19 epidemic a global health emergency on January 30 and confirmed its transformation into a pandemic on March 11. China has been the hardest hit since the virus's outbreak, which may date back to late November. Saudi Arabia realized the danger of the Coronavirus in March 2020, took the initiative to take a set of pre-emptive decisions that preceded many countries of the world, and worked to harness all capabilities to confront the outbreak of the epidemic. Several researchers are currently using various mathematical and machine learning-based prediction models to estimate this pandemic's future trend. In this work, the SEIR model was applied to predict the epidemic situation in Saudi Arabia and evaluate the effectiveness of some epidemic control measures, and finally, providing some advice on preventive measures.</p>

scholarly journals A Normalized Mortality Rate Showed the Diverse Severity of COVID-19 and Its Association With Other Chronic Diseases

2020 ◽  
Author(s):  
Santosh Kumar ◽  
P.R. Renjith ◽  
C. Priscilla ◽  
Selva Kumar Ganesan
Keyword(s):  
Cardiovascular Diseases ◽  
Mortality Rate ◽  
Chronic Diseases ◽  
Respiratory Diseases ◽  
Correlation Coefficients ◽  
The United States ◽  
World Health ◽  
The World ◽  
The Usa ◽  
Health Organization

Covid-19 has given a halt to all the activities in the world. Europe was most affected, followed by the United States of America. It has taken more than 225,000 lives until now. In this study, we have assessed the severity of Covid-19 by analyzing the mortality rate of Covid-19 and other chronic diseases. The Covid-19 data and &ldquo;death rate&rdquo; data caused by other diseases were downloaded from the world health organization (WHO) website. A normalized method was used to see the mortality rate of Covid-19 in comparison to other diseases. The deaths caused by Covid-19 in April 2020 have overtaken the average number of deaths caused by Cancer, Cardiovascular diseases, and other diseases in Belgium, Spain, France, Italy, the UK, and Ireland. Covid-19 was found to be strongly correlated with non-communicable respiratory diseases and Cancer with correlation coefficients 0.73 and 0.70 respectively. The severity of Covid-19 in the USA was moderate. The severity of Covid-19 in Asian countries was found to be low. Europe showed the highest diversity in the mortality rate of Covid-19. On average, except for a few European countries, Cardiovascular diseases, cancer, and non-communicable respiratory diseases were still more lethal and caused more deaths than Covid-19.

scholarly journals A Normalized Mortality Rate Showed the Diverse Severity of COVID-19 and Its Association With Other Chronic Diseases

2020 ◽  
Author(s):  
Santosh Kumar ◽  
P.R. Renjith ◽  
C. Priscilla ◽  
Selva Kumar Ganesan
Keyword(s):  
Cardiovascular Diseases ◽  
Mortality Rate ◽  
Chronic Diseases ◽  
Respiratory Diseases ◽  
The United States ◽  
World Health ◽  
Asian Countries ◽  
The World ◽  
The Usa ◽  
Health Organization

Covid-19 has given a halt to all the activities in the world. Europe was most affected followed by the United States of America. It has taken more than 200,000 lives till now. In this study, we have assessed the severity of Covid-19 by analyzing the mortality rate in Covid-19 and other diseases to see the severity of Covid-19 and other chronic diseases. The Covid-19 data and &ldquo;death rate&rdquo; data caused by other diseases were downloaded from the world health organization (WHO) website. A normalized period based method was used to see the mortality rate of Covid-19 in comparison to other diseases. The deaths occurred by cardiovascular diseases, cancer, and respiratory diseases were more in number than the Covid-19 caused deaths in the 45 days period where most of the Covid-19 deaths had taken place. The severity of Covid-19 in the USA was moderate. The severity of Covid-19 in Asian countries was found to be at a low. Europe showed the highest diversity in the mortality rate of Covid-19. Cardiovascular diseases, cancer, and non-communicable diseases were still more lethal and caused more deaths than Covid-19.

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 Molluscicidal activity of crown of Christ (Euphorbia splendens var. hislopii) (Euphorbiaceae) latex submitted to pH variation

2003 ◽  
Vol 46 (3) ◽  
pp. 415-420 ◽  
Author(s):  
Maurício Carvalho Vasconcellos ◽  
José Augusto Albuquerque dos Santos ◽  
Ivonise Paz da Silva ◽  
Fátima Eliana Ferreira Lopes ◽  
Virgínia Torres Schall
Keyword(s):  
Mortality Rate ◽  
Aqueous Solutions ◽  
Schistosoma Mansoni ◽  
World Health Organization ◽  
Biomphalaria Glabrata ◽  
World Health ◽  
Ph Variation ◽  
Health Organization ◽  
Minor Concentration ◽  
Molluscicidal Activity

Laboratory and field bioassays have confirmed the specificity of the molluscicidal activity of the Euphorbia splendens var. hislopii latex (crown of Christ) (Euphorbiaceae) over snails of the species Biomphalaria glabrata, B. tenagophila, B. straminea, B. pfeifferi and Bulinus sp. in the control of Schistosoma mansoni. In the present study, the effect of the pH variation on lethal concentration (LC90) over B. tenagophila was evaluated. Bioassays with the aqueous solutions of the latex ranging from 0.4 to 12 µl/l were adjusted for pH of 5.0; 6.0; 7.0 and 8.0, and tested in accordance with methods standardized by World Health Organization. The results obtained indicated that the minor concentration of the latex occurred at pH 6.0 (LC90 = 3.2 µl/l) and the maximum at pH 8.0 (LC90 = 10.3 µl/l). Lethal concentrations adjusted for pH 5.0 and 7.0 were 3.4 µl/l and 4,7µl/l, respectively. From the results it could be concluded that the molluscicidal toxicity was not altered when the concentrations were adjusted for pH 5.0 and 6.0, as we observed that mortality rate was 100% starting at a concentration of 2.0 µl/l, not the same for the concentrations with adjustment for pH 7.0 and 8.0.

scholarly journals Literature Review The Relationship Of Antenatal Class Towards Mothers’ Knowledge Of DangerousSign During Pregnancy

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Ayu Kurniati ◽  
Enny Fitriahadi
Keyword(s):  
Developing Countries ◽  
Mortality Rate ◽  
Literature Review ◽  
World Health ◽  
Live Births ◽  
Complication Risk ◽  
The World ◽  
Health Organization ◽  
Relationship Of ◽  
The Relationship

IN 2013, the World Health Organization, released data in the form of Maternal Mortality Rate (MMR) worldwide, and the number reached 289,000 per 100, 000 live births, which 99% of cases occurred in developing countries. Research aims to discover the relationship of antenatal class towards mothers’ knowledge of the dangerous sign during pregnancy. The result showed that there is a relationship of antenatal class towards mothers’ knowledge of dangerous sign during pregnancy, From this result, the researcher concludes that antenatal class could increase mothers’ knowledge of dangerous sign during pregnancy and may decrease the complication risk during the childbirth.

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

scholarly journals Machine Learning based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

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