scholarly journals Machine Learning of Medical Applications Involving Complicated Proteins and Genetic Measurements

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Malik Bader Alazzam ◽  
Hoda Mansour ◽  
Mohamed M. Hammam ◽  
Said Alsheikh ◽  
Ali Bakir ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Information Gain ◽  
Extraction Methods ◽  
Detection Algorithm ◽  
World Health ◽  
Medical Applications ◽  
Detection Accuracy ◽  
Pca Method ◽  
Health Organization

Motivations. Breast cancer is the second greatest cause of cancer mortality among women, according to the World Health Organization (WHO), and one of the most frequent illnesses among all women today. The influence is not confined to industrialized nations but also includes emerging countries since the authors believe that increased urbanization and adoption of Western lifestyles will lead to a rise in illness prevalence. Problem Statement. The breast cancer has become one of the deadliest diseases that women are presently facing. However, the causes of this disease are numerous and cannot be properly established. However, there is a huge difficulty in not accurately recognizing breast cancer in its early stages or prolonging the detection process. Methodology. In this research, machine learning is a field of artificial intelligence that employs a variety of probabilistic, optimization, and statistical approaches to enable computers to learn from past data and find and recognize patterns from large or complicated groups. The advantage is particularly well suited to medical applications, particularly those involving complicated proteins and genetic measurements. Result and Implications. However, when using the PCA method to reduce the features, the detection accuracy dropped to 89.9%. IG-ANFIS gave us detection accuracy (98.24%) by reducing the number of variables using the “information gain” method. While the ANFIS algorithm had a detection accuracy of 59.9% without utilizing features, J48, which is one of the decision tree approaches, had a detection accuracy of 92.86% without using features extraction methods. When applying PCA techniques to minimize features, the detection accuracy was lowered to the same way (91.1%) as the Naive Bayes detection algorithm (96.4%).

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.

T26248G-transversion mutation in exon7 of the putative methyltransferase Nsun7 gene causes a change in protein folding associated with reduced sperm motility in asthenospermic men

2015 ◽  
Vol 27 (3) ◽  
pp. 471 ◽  
Author(s):  
Nahid Khosronezhad ◽  
Abasalt Hosseinzadeh Colagar ◽  
Syed Golam Ali Jorsarayi
Keyword(s):  
Protein Folding ◽  
Sperm Motility ◽  
Protein Function ◽  
Direct Sequencing ◽  
Protein Structures ◽  
Single Strand Conformation Polymorphism ◽  
Extraction Methods ◽  
World Health ◽  
Transversion Mutation ◽  
Health Organization

The NOP2/Sun domain family, member 7 (Nsun7) gene, which encodes putative methyltransferase Nsun7, has a role in sperm motility in mice. In humans, this gene is located on chromosome 4 with 12 exons. The aim of the present study was to investigate mutations of exon 7 in the normospermic and asthenospermic men. Semen samples were collected from the Fatemezahra IVF centre (Babol, Iran) and analysed on the basis of World Health Organization (WHO) guidelines using general phenol–chloroform DNA extraction methods. Exon 7 was amplified using Sun7-F and Sun7-R primers. Bands on samples from asthenospermic men that exhibited different patterns of movement on single-strand conformation polymorphism gels compared with normal samples were identified and subjected to sequencing for further identification of possible mutations. Direct sequencing of polymerase chain reaction (PCR) products, along with their analysis, confirmed C26232T-transition and T26248G-transversion mutations in asthenospermic men. Comparison of normal and mutant protein structures of Nsun7 indicated that the amino acid serine was converted to alanine, the structure of the helix, coil and strand was changed, and the protein folding and ligand binding sites were changed in samples from asthenospermic men with a transversion mutation in exon 7, indicating impairment of protein function. Because Nsun7 gene products have a role in sperm motility, if an impairment occurs in exon 7 of this gene, it may lead to infertility. The transversion mutation in exon 7 of the Nsun7 gene can be used as an infertility marker in asthenospermic men.

scholarly journals HUBUNGAN PENGETAHUAN DAN SIKAP DENGAN TINDAKAN PERSONAL HYGIENE SAATMENSTRUASI PADA REMAJA PUTRI

2018 ◽  
Vol 9 (2) ◽  
pp. 165-170
Author(s):  
Husni Husni
Keyword(s):  
Breast Cancer ◽  
Cervical Cancer ◽  
Random Sampling ◽  
World Health ◽  
Personal Hygiene ◽  
Bivariate Analysis ◽  
Stratified Random Sampling ◽  
The World ◽  
Health Organization

One of the effects is not maintaining hygiene during menstruation is able to hitkankes Rahim neck (cervical). Based on data from the World Health Organization (WHO),cervical cancer is the second most cancer in women aged 15-45 years after breast cancer. Noless than 500,000 new cases with 280,000 patient deaths occur each year worldwide.Indonesia was ranked first by the victims died at least 555 women per day and 200,000women annually. This study aims to determine the correlation between knowledge andattitude towards personal hygiene during menstruation action at SMAN 2 Bengkulu City. Thisresearch is descriptive analytic. The number of respondents 84 people with a samplingtechnique that stratified random sampling. Presentation of data is done by using a frequencydistribution table. The collection of data taken using a questionnaire. The data were analyzedusing univariate and bivariate analysis with Chi-Square.The results showed that therespondents are classified as good knowledge of (54.8%), attitude unfavorabel or does notsupport (53.6%), and the biggest acts (52.4%) is good. From the bivariate analysis were foundno correlation between knowledge against acts of personal hygiene during menstruation (p =0.794), and no relation attitude towards personal hygiene actions during menstruation (p =0.975).Required role of schools, educators, parents to be more proactive in enhancingknowledge and useful information about the process of menstruation and how to maintainhygiene during menstruation.

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 Spatial Distribution of Fluoride in Drinking Water in Dhamar City, Yemen

2021 ◽  
Vol 10 (1) ◽  
pp. 49-63
Author(s):  
Hefdhallah Al Aizari ◽  
Rachida Fegrouche ◽  
Ali Al Aizari ◽  
Saeed S. Albaseer
Keyword(s):  
Drinking Water ◽  
Anthropogenic Activities ◽  
Groundwater Resources ◽  
Principal Component ◽  
World Health ◽  
Residential Areas ◽  
Central Plateau ◽  
The World ◽  
Pca Method ◽  
Health Organization

The fact that groundwater is the only source of drinking water in Yemen mandates strict monitoring of its quality. The aim of this study was to measure the levels of fluoride in the groundwater resources of Dhamar city. Dhamar city is the capital of Dhamar governorate located in the central plateau of Yemen. For this purpose, fluoride content in the groundwater from 16 wells located around Dhamar city was measured. The results showed that 75% of the investigated wells contain fluoride at or below the permissible level set by the World Health Organization (0.5 – 1.5 mg/L), whereas 25% of the wells have relatively higher fluoride concentrations (1.59 – 184 mg/L). The high levels of fluoride have been attributed to the anthropogenic activities in the residential areas near the contaminated wells. Interestingly, some wells contain very low fluoride concentrations (0.30 – 0.50 mg/L).  Data were statistically treated using the principal component analysis (PCA) method to investigate any possible correlations between various factors. PCA shows a high correlation between well depth and its content of fluoride. On the other hand, health problems dominating in the study area necessitate further studies to investigate any correlation with imbalanced fluoride intake.

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.

Pneumonia Detection Using Deep Learning Architectures

2020 ◽  
Vol 17 (12) ◽  
pp. 5535-5542
Author(s):  
Purohit Om Hemantkumar ◽  
Rakshit Lodha ◽  
Meghna Bajoria ◽  
R. Sujatha
Keyword(s):  
Feature Extraction ◽  
Imaging System ◽  
Extraction Methods ◽  
World Health ◽  
X Rays ◽  
Wavelet Frame ◽  
Training Models ◽  
Wavelet Frame Transform ◽  
Health Organization ◽  
Learning Architectures

Pneumonia is an infection caused by bacteria and viruses. It can shift from mellow to serious cases. This disease causes severe damages to the lungs since they fill with fluids. This situation causes difficulty in breathing. It further prevents oxygen to reach the blood. Pneumonia is diagnosed with the help of a chest X-rays, which can also use in the diagnosis of diseases like emphysema, lung cancer, and tuberculosis. According to WHO (World Health Organization (WHO). 2001. Standardization of Interpretation of Chest Radiographs for the Diagnosis of Pneumonia in Children. p.4.), Chest X-rays, at present, is the best available method for detecting pneumonia. Feature extraction methods like DiscreteWavelet Transform (DWT),Wavelet Frame Transform (WFT), andWavelet Packet Transform (WPT) can be used followed by any classification algorithm. In this paper, models like Squeezenet, DenseNet, and Resnet34 have been used for image recognition. In our system, the medical images were taken from Kaggle database and were recorded using a suitable imaging system. The images retrieved were then considered for input for the system where the images go through the various phases of image processing like pre-processing, edge detection and feature extraction. Later, a variety of training models are applied to know which model offers the highest accuracy.

Web-Based Machine Learning Application for Heart Disease Prediction

2022 ◽  
pp. 383-393
Author(s):  
Lokesh M. Giripunje ◽  
Tejas Prashant Sonar ◽  
Rohit Shivaji Mali ◽  
Jayant C. Modhave ◽  
Mahesh B. Gaikwad
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Web Application ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Web Based ◽  
The World ◽  
Comprehensive Survey ◽  
Health Organization

Risk because of heart disease is increasing throughout the world. According to the World Health Organization report, the number of deaths because of heart disease is drastically increasing as compared to other diseases. Multiple factors are responsible for causing heart-related issues. Many approaches were suggested for prediction of heart disease, but none of them were satisfactory in clinical terms. Heart disease therapies and operations available are so costly, and following treatment, heart disease is also costly. This chapter provides a comprehensive survey of existing machine learning algorithms and presents comparison in terms of accuracy, and the authors have found that the random forest classifier is the most accurate model; hence, they are using random forest for further processes. Deployment of machine learning model using web application was done with the help of flask, HTML, GitHub, and Heroku servers. Webpages take input attributes from the users and gives the output regarding the patient heart condition with accuracy of having coronary heart disease in the next 10 years.

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.

Systemic Therapy

2019 ◽  
pp. 176-193
Author(s):  
Didier Verhoeven ◽  
Etienne Brain ◽  
François Duhoux ◽  
Gilberto Schwartsmann ◽  
Fatima Cardoso
Keyword(s):  
Breast Cancer ◽  
Patient Reported Outcome ◽  
World Health ◽  
Key Factors ◽  
Process Indicators ◽  
Funding Model ◽  
Day Clinic ◽  
Patient Reported ◽  
Health Organization ◽  
The Given

Abstract: Quality management of systemic treatment of breast cancer is a high priority, but few quality indicators have been identified so far. As structure indicators, education, day clinic facilities, and a dedicated pharmacy are proposed. As process indicators, access to appropriate experts, equity, and report of the given treatment in accordance to the guidelines are suggested. Overuse can burden the patients with unnecessary toxicity and society with unnecessary costs. Overall survival is the reference standard of outcome, but this is difficult to compare. Just as important are quality of life, patient-reported outcome, and safety. The essential antineoplastic agents for breast cancer are regularly updated by the World Health Organization. Tailored therapy, optimal patient selection, and clinical benefit are becoming key factors for all patients. In the future, a new funding model, precision medicine, and the molecular revolution will require sufficient human resources and networks to organize the best strategies.

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