scholarly journals An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms

2013 ◽  
Vol 112 (3) ◽  
pp. 441-454 ◽  
Author(s):  
Jorge L.M. Amaral ◽  
Agnaldo J. Lopes ◽  
José M. Jansen ◽  
Alvaro C.D. Faria ◽  
Pedro L. Melo
Keyword(s):  
Machine Learning ◽  
Early Diagnosis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Improved Method

Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms

2020 ◽  
Vol 46 (3) ◽  
pp. 454-462 ◽  
Author(s):  
Michael Roimi ◽  
Ami Neuberger ◽  
Anat Shrot ◽  
Mical Paul ◽  
Yuval Geffen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Early Diagnosis ◽  
Learning Algorithms ◽  
Bloodstream Infections ◽  
Machine Learning Algorithms

Statistical Perspectives and Machine Learning Algorithms

2021 ◽  
pp. 315-338
Author(s):  
N. Ajaypradeep ◽  
R. Sasikala
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Early Diagnosis ◽  
Learning Algorithms ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Technological Support ◽  
Daily Routines ◽  
Preterm Born

Autism unlike other diseases has peculiar symptoms and pre-causes. The symptoms and suspicions are found especially in newly born children, preterm born infants, and children below 12 years. These children have peculiar attributes such as inability to communicate with fellow children, poor speech ability, difficulty in dealing with daily routines and procedures and being oversensitive. This study correlates with the existing work on autism diagnosis techniques by using machine learning methodologies. It further provides the summary of the relevant techniques to validate the existence of autism disorder and strategies used for diagnosis. Various diagnostic methods include behavioural analysis, eye tracking, and neural or brain imaging. The key objective of the chapter is to assess and understand the preliminary causes of the autism spectrum disorder, including analyzing technological support that can be rendered for the early diagnosis of autism.

scholarly journals Classification of Epileptic and Normal EEG Signals Using Power Spectrum of Sub-bands

2020 ◽  
pp. 6-9
Author(s):  
Sude Pehlivan ◽  
Yalcin Isler
Keyword(s):  
Machine Learning ◽  
Early Diagnosis ◽  
Power Spectrum ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Eeg Signals ◽  
Power Sum ◽  
Band Power ◽  
Time And Energy

The early diagnosis of epilepsy, which affects the lives of many people worldwide, is the first step of treatment to help patients to continue their lives efficiently. Experts have to spend a lot of time and energy to make this diagnosis as quickly and accuratelyaspossible.The aimofthisstudywasto investigatethe capacity of machine learning algorithms to distinguish epileptic and normal signals to develop a system that can automatically diagnose seizures. LabVIEW was used to obtain the sum of EEG sub-band powers which were used as an attribute for both epileptic and normal records. These attributes were classified with different classifiers using Matlab and as a result of the classification, it was concluded that the sub-band power sum can be used as a meaningful attribute in the classification of epileptic and normal EEG signals.

scholarly journals A Review of COVID-19 Diagnostic Methods

2020 ◽  
Vol 7 (4) ◽  
Author(s):  
Reyhaneh Yaghobzadeh ◽  
Seyyed Reza Kamel ◽  
Koresh Shirazi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Economic Problems ◽  
Important Concern ◽  
The World

: The new coronavirus disease 2019 (COVID-19) has recently emerged as an acute respiratory syndrome. The virus has spread throughout the world since the primary outbreak of the disease reported in Wuhan, China. The pandemic has led to increased mortality as the most important threat of the disease in specific populations across the world. Furthermore, COVID-19 has caused significant economic problems in several countries. The early diagnosis of COVID-19 is currently an important concern for physicians and communities. The present study aimed to review the published articles regarding the diagnosis of COVID-19 until the end of February 2020. According to the results we show that deep learning and machine learning algorithms can be effectively used to the scope of the disease.

scholarly journals Machine Learning Approach for Automatic Detection of Alzheimers Disease using Resting State fMRI

2021 ◽  
Vol 9 (VI) ◽  
pp. 5399-5409
Author(s):  
K. Emily Esther Rani
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Resting State ◽  
Learning Algorithms ◽  
Resting State Fmri ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Discrete Wavelet

Alzheimer’s Disease (AD) is a neurological disease that affects memory and the livelihood of the people that are diagnosed with it. Efficient automated techniques for early diagnosis of AD is very important because early diagnosis is used to prevent a patient from death. In this work, we present a novel computer-aided diagnosis (CAD) techniques using machine learning algorithms for the early diagnosis of AD. The input resting state fMRI(rsfMRI) images are taken from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The input image is pre-processed using Discrete Wavelet Transform(DWT). Automated thresholding algorithm is used to segment the image. Then, the segmented resting state fMRI images are used to extract useful and informative features. The best features are selected by Fisher’s code feature selection algorithm. Finally, an automated Image classification step is performed using machine learning algorithms Support Vector Machine(SVM), Decision Tree , Random Forest and Multi-Layer Perceptron algorithms to distinguish between normal patients and AD patients.

Sign in / Sign up

Export Citation Format

Share Document