Early Detection of Bipolar Disorder Four Years Before Onset in a 22-Year Population-Based Birth Cohort Using Advanced Machine Learning Techniques

2020 ◽  
Author(s):  
Francisco Diego Rabelo-da-Ponte ◽  
Jacson Gabriel Feiten ◽  
Benson Mwangi ◽  
Fernando C. Barros ◽  
Fernando C. Wehrmeister ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Early Detection ◽  
Birth Cohort ◽  
Population Based ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective

2021 ◽  
Vol 8 ◽  
Author(s):  
Daniele Roberto Giacobbe ◽  
Alessio Signori ◽  
Filippo Del Puente ◽  
Sara Mora ◽  
Luca Carmisciano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Prediction Models ◽  
Early Recognition ◽  
Model Performance ◽  
Daily Practice ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Long Run ◽  
Learning Techniques

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.

scholarly journals Review on Machine Learning Techniques to predict Bipolar Disorder

2021 ◽  
Author(s):  
Nisha Agnihotri
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Bipolar Disorder ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Brain Disorder ◽  
Research Papers ◽  
Learning Techniques

<i>Bipolar disorder, a complex disorder in brain has affected many millions of people around the world. This brain disorder is identified by the occurrence of the oscillations of the patient’s changing mood. The mood swing between two states i.e. depression and mania. This is a result of different psychological and physical features. A set of psycholinguistic features like behavioral changes, mood swings and mental illness are observed to provide feedback on health and wellness. The study is an objective measure of identifying the stress level of human brain that could improve the harmful effects associated with it considerably. In the paper, we present the study prediction of symptoms and behavior of a commonly known mental health illness, bipolar disorder using Machine Learning Techniques. Therefore, we extracted data from articles and research papers were studied and analyzed by using statistical analysis tools and machine learning (ML) techniques. Data is visualized to extract and communicate meaningful information from complex datasets on predicting and optimizing various day to day analyses. The study also includes the various research papers having machine Learning algorithms and different classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed for identifying the mental state in a target group. The purpose of the paper is mainly to explore the challenges, adequacy and limitations in detecting the mental health condition using Machine Learning Techniques</i>

scholarly journals Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques

2021 ◽  
Author(s):  
Mehmet Akif Cifci
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
The Other ◽  
Machine Learning Techniques ◽  
Image Processing Techniques ◽  
Learning Techniques ◽  
Fundus Photographs ◽  
Processing Techniques ◽  
Time And Energy

The complication of people with diabetes causes an illness known as Diabetic Retinopathy (DR). It is very widespread among middle-aged and older people. As diabetes progresses, patients' vision may deteriorate and cause DR. People to lose their vision because of this illness. To cope with DR, early detection is needed. Patients will have to be checked by doctors regularly, which is a waste of time and energy. DR can be divided into two groups: non-proliferative (NPDR) while the other is proliferative (PDR). In this study, machine learning (ML) techniques are used to diagnose DR early. These are PNN, SVM, Bayesian Classification, and K-Means Clustering. These techniques will be evaluated and compared with each other to choose the best methodology. A total of 300 fundus photographs are processed for training and testing. The features are extracted from these raw images using image processing techniques. After an experiment, it is concluded that PNN has an accuracy of about 89%, Bayes Classifications 94%, SVM 97%, and K-Means Clustering 87%. The preliminary results prove that SVM is the best technique for early detection of DR.

scholarly journals Review on Machine Learning Techniques to predict Bipolar Disorder

2021 ◽  
Author(s):  
Nisha Agnihotri
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Bipolar Disorder ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Brain Disorder ◽  
Research Papers ◽  
Learning Techniques

<i>Bipolar disorder, a complex disorder in brain has affected many millions of people around the world. This brain disorder is identified by the occurrence of the oscillations of the patient’s changing mood. The mood swing between two states i.e. depression and mania. This is a result of different psychological and physical features. A set of psycholinguistic features like behavioral changes, mood swings and mental illness are observed to provide feedback on health and wellness. The study is an objective measure of identifying the stress level of human brain that could improve the harmful effects associated with it considerably. In the paper, we present the study prediction of symptoms and behavior of a commonly known mental health illness, bipolar disorder using Machine Learning Techniques. Therefore, we extracted data from articles and research papers were studied and analyzed by using statistical analysis tools and machine learning (ML) techniques. Data is visualized to extract and communicate meaningful information from complex datasets on predicting and optimizing various day to day analyses. The study also includes the various research papers having machine Learning algorithms and different classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed for identifying the mental state in a target group. The purpose of the paper is mainly to explore the challenges, adequacy and limitations in detecting the mental health condition using Machine Learning Techniques</i>

A Statistical Scrutiny of Three Prominent Machine-Learning Techniques to Forecast Machining Performance Parameters of Inconel 690

2018 ◽  
pp. 104-120
Author(s):  
Binayak Sen ◽  
Uttam Kumar Mandal ◽  
Sankar Prasad Mondal
Keyword(s):  
Machine Learning ◽  
Population Based ◽  
Black Box ◽  
Machine Learning Techniques ◽  
Percentage Error ◽  
Machining Parameters ◽  
Ann Model ◽  
Machining Performance ◽  
Learning Techniques ◽  
Inconel 690

Computational approaches like “Black box” predictive modeling approaches are extensively used technique applied in machine learning operations of today. Considering the latest trends, present study compares capabilities of two different “Black box” predictive model like ANFIS and ANN with a population-based evolutionary algorithm GEP for forecasting machining parameters of Inconel 690 material, machined in a CNC-assisted 3-axis milling machine. The aims of this article are to represent considerable data showing, every techniques performance under the criteria of root mean square error (RSME), Correlational coefficient R and Mean absolute percentage error (MAPE). In this chapter, we vigorously demonstrate that the performance of the GEP model is far superior to ANFIS and ANN model.

Measuring inflation expectations ofthe Russian population with the help of machine learning

2017 ◽  
pp. 71-93 ◽  
Author(s):  
I. Goloshchapova ◽  
M. Andreev
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Population Based ◽  
Russian Population ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Inflation Expectations ◽  
New Approach ◽  
Learning Techniques ◽  
The Web

The paper proposes a new approach to measure inflation expectations of the Russian population based on text mining of information on the Internet with the help of machine learning techniques. Two indicators were constructed on the base of readers’ comments to inflation news in major Russian economic media available in the web at the period from 2014 through 2016: with the help of words frequency and sentiment analysis of comments content. During the whole considered period of time both indicators were characterized by dynamics adequate to the development of macroeconomic situation and were also able to forecast dynamics of official Bank of Russia indicators of population inflation expectations for approximately one month in advance.

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