scholarly journals Early Prediction of Autism Spectrum Disorder by Computational Approaches to fMRI Analysis with Early Learning Technique

2020 ◽  
Vol 2 (4) ◽  
pp. 207-216
Author(s):  
Karunakaran P ◽  
Yasir Babiker Hamdan ◽  
Sathish
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Brain Activity ◽  
Early Learning ◽  
Autism Spectrum ◽  
Prediction Methods ◽  
Machine Learning Algorithm ◽  
Early Prediction ◽  
Neuro Imaging ◽  
Fmri Analysis

The neuro imaging developmental classification studies are undergone with small amount of samples from the brain activity samples. It promises the inspiring complications in high dimensional data analysis. Autism prediction methodologies are based on behavioral function alone previously which provides good precision but repossession will be unfortunate. We address those problems for early prediction of autism with neural development modern techniques and compared with older. Moreover, visualization of brain activities is quite important in neuro imaging. We believe in better visualization and classification of neuro images in early month captures and appended of Mullen Scales of Early Learning (MSEL). Functional magnetic resonance imaging (fMRI) is one of the controlling tools for measuring non-invasively measure brain activity and it provides with good resolution. For high resolution of brain activity, fMRI gives better than electro encephalon graph (EEG). Visualization of brain activity very clearly is first step to recognize the faults of autism. We have taken into the account for predicting in early Autism Spectrum Disorder (ASD) with help of multiple behavioral activities and development measures using machine learning algorithm. The prediction methods are examined with mostly many prediction methods start to examine the neuro imaging with ultra-high risk factors. The prediction of ASD is moderate accuracy in 14 month development measures from multiple time points. In this proposed work, Mullen early prediction is appended for early prediction and it is examined with computational approach to fMRI analysis with adaptive functioning classifier for machine learning algorithm. This proposed algorithm provides improved version of classification in machine languages with MSEL and high accuracy with conservative methods.

scholarly journals Silent Hypoxia in Covid-19: A Machine Learning Algorithm for Early Prediction

2021 ◽  
Vol 233 (5) ◽  
pp. e191
Author(s):  
Zain I. Khalpey ◽  
Amina Khalpey ◽  
Bhavisha Modi ◽  
Jessa L. Deckwa
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Early Prediction

scholarly journals Machine Learning Approaches for Early Prediction of Gestational Diabetes Mellitus Based on Prospective Cohort Study

2021 ◽  
Author(s):  
Jingyuan Wang ◽  
Xiujuan Chen ◽  
Yueshuai Pan ◽  
Kai Chen ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Cohort Study ◽  
Gestational Diabetes ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Machine Learning Algorithm ◽  
Early Prediction ◽  
Test Dataset ◽  
Svm Model

Abstract Purpose: To develop and verify an early prediction model of gestational diabetes mellitus (GDM) using machine learning algorithm.Methods: The dataset collected from a pregnant cohort study in eastern China, from 2017 to 2019. It was randomly divided into 75% as the training dataset and 25% as the test dataset using the train_test_split function. Based on Python, four classic machine learning algorithm and a New-Stacking algorithm were first trained by the training dataset, and then verified by the test dataset. The four models were Logical Regression (LR), Random Forest (RT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The sensitivity, specificity, accuracy, and area under the Receiver Operating Characteristic Curve (AUC) were used to analyse the performance of models.Results: Valid information from a total of 2811 pregnant women were obtained. The accuracies of the models ranged from 80.09% to 86.91% (RF), sensitivities ranged from 63.30% to 81.65% (SVM), specificities ranged from 79.38% to 97.53% (RF), and AUCs ranged from 0.80 to 0.82 (New-Stacking).Conclusion: This paper successfully constructed a New-Stacking model theoretically, for its better performance in specificity, accuracy and AUC. But the SVM model got the highest sensitivity, the SVM model was recommends as the prediction model for clinical.

scholarly journals House Price Prediction Using Machine Learning

2019 ◽  
Vol 8 (9) ◽  
pp. 717-722 ◽  
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Housing Cost ◽  
Prediction Methods ◽  
Machine Learning Algorithm ◽  
Lasso Regression ◽  
Cost Prediction ◽  
Price Prediction

Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Machine learning system also provides better customer service and safer automobile systems. In the present paper we discuss about the prediction of future housing prices that is generated by machine learning algorithm. For the selection of prediction methods we compare and explore various prediction methods. We utilize lasso regression as our model because of its adaptable and probabilistic methodology on model selection. Our result exhibit that our approach of the issue need to be successful, and has the ability to process predictions that would be comparative with other house cost prediction models. More over on other hand housing value indices, the advancement of a housing cost prediction that tend to the advancement of real estate policies schemes. This study utilizes machine learning algorithms as a research method that develops housing price prediction models. We create a housing cost prediction model In view of machine learning algorithm models for example, XGBoost, lasso regression and neural system on look at their order precision execution. We in that point recommend a housing cost prediction model to support a house vender or a real estate agent for better information based on the valuation of house. Those examinations exhibit that lasso regression algorithm, in view of accuracy, reliably outperforms alternate models in the execution of housing cost prediction.

scholarly journals Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0168224 ◽  
Author(s):  
Matthew J. Maenner ◽  
Marshalyn Yeargin-Allsopp ◽  
Kim Van Naarden Braun ◽  
Deborah L. Christensen ◽  
Laura A. Schieve
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Learning Algorithm ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Algorithm

scholarly journals More similarities than differences between ADHD and ASD in functional brain connectivity

2020 ◽  
Author(s):  
Joe Bathelt ◽  
Matthan Caan ◽  
Hilde Geurts
Keyword(s):  
Machine Learning ◽  
Functional Neuroimaging ◽  
Learning Algorithm ◽  
Brain Connectivity ◽  
Total Sample ◽  
Autism Spectrum ◽  
Future Research ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Functional Brain

Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly comorbid neurodevelopmental conditions. There is an ongoing debate regarding the nature of their overlap. Behavioral symptoms and cognitive profiles indicate differences between the conditions, but genetic studies and neuroimaging investigations suggest at least some shared etiology. The current study investigated if functional connectivity can be used to distinguish ADHD and ASD using a machine-learning approach. Towards this aim, we trained a machine learning algorithm to distinguish ASD and ADHD cases from each other and from comparison cases in a total sample of 805 cases, comprising of 243 ASD cases, 164 ADHD cases, and 398 comparison cases between 7 and 21 years of age. We compared the performance of the best performing machine learning algorithm (l2-regularised support vector classification) when classifying unseen cases of ADHD, ASD, and CMP. The results indicated lower classification performance when distinguishing ADHD from ASD compared to classifying diagnostic groups vs a typical comparison group. The model trained to distinguish ASD and comparison cases performed equally well when tasked with classifying ADHD vs CMP. A Bayesian analysis gave strong evidence for similarity ADHD and ASD. The ADHD and ASD group showed overlap in connections of the right ventral attention network, the salience network, and the default mode network. In sum, these results suggest a substantial overlap in functional brain connectivity between ADHD and ASD. We discuss the implications of these findings for the quest to identify functional neuroimaging biomarkers and provide recommendation for future research.

scholarly journals Machine learning algorithm for improving performance on 3 AQ-screening classification

2019 ◽  
Vol 4 (2) ◽  
pp. 44-49
Author(s):  
Taftazani Ghazi Pratama ◽  
Rudy Hartanto ◽  
Noor Akhmad Setiawan
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Autism Spectrum ◽  
Support Vector ◽  
Autism Spectrum Quotient ◽  
Machine Learning Algorithm ◽  
Study Support ◽  
Artificial Neural Network Ann ◽  
Fold Cross Validation

Autism Spectrum Disorder (ASD) classification using machine learning can help parents, caregivers, psychiatrists, and patients to obtain the results of early detection of ASD. In this study, the dataset used is the autism-spectrum quotient for child, adolescent and adult, namely AQ-child, AQ-adolescent, AQ-adult. This study aims to improve the sensitivity and specificity of previous studies so that the classification results of ASD are better characterized by the reduced misclassification. The algorithm applied in this study: support vector machine (SVM), random forest (RF), artificial neural network (ANN). The evaluation results using 10-fold cross validation showed that RF succeeded in producing higher adult AQ sensitivity, which was 87.89%. The increase in the specificity level of AQ-Adolescents is better produced using an SVM of 86.33%.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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