Machine Learning Techniques for AD/MCI Diagnosis and Prognosis

2013 ◽  
pp. 147-179 ◽  
Author(s):  
Dinggang Shen ◽  
Chong-Yaw Wee ◽  
Daoqiang Zhang ◽  
Luping Zhou ◽  
Pew-Thian Yap
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Diagnosis And Prognosis ◽  
Learning Techniques

Role of Artificial Intelligence in Modeling Psychometrics to Detect Neurodevelopmental Disorders

2020 ◽  
pp. 213-227
Author(s):  
Navjot Singh ◽  
Amarjot Kaur
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neurodevelopmental Disorders ◽  
Machine Learning Techniques ◽  
Diagnosis And Prognosis ◽  
Processing Power ◽  
Learning Techniques ◽  
Numerous Data ◽  
Applications Of Machine Learning ◽  
Micro Patterns

The objective of the present chapter is to highlight applications of machine learning and artificial intelligence (AI) in clinical diagnosis of neurodevelopmental disorders. The proposed approach aims at recognizing behavioral traits and other cognitive aspects. The availability of numerous data and high processing power, such as graphic processing units (GPUs) or cloud computing, enabled the study of micro-patterns hundreds of times faster compared to manual analysis. AI, being a new technological breakthrough, enables study of human behavior patterns, which are hidden in millions of micro-patterns originating from human actions, reactions, and gestures. The chapter will also focus on the challenges in existing machine learning techniques and the best possible solution addressing those problems. In the future, more AI-based expert systems can enhance the accuracy of the diagnosis and prognosis process.

Prediction of depression cases, incidence, and chronicity in a large occupational cohort using machine learning techniques: an analysis of the ELSA-Brasil study

2020 ◽  
pp. 1-9
Author(s):  
Diego Librenza-Garcia ◽  
Ives Cavalcante Passos ◽  
Jacson Gabriel Feiten ◽  
Paulo A. Lotufo ◽  
Alessandra C. Goulart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elastic Net ◽  
Machine Learning Techniques ◽  
Adult Health ◽  
Recurrent Depression ◽  
Elastic Net Regularization ◽  
Diagnosis And Prognosis ◽  
Learning Techniques ◽  
Occupational Cohort

Abstract Background Depression is highly prevalent and marked by a chronic and recurrent course. Despite being a major cause of disability worldwide, little is known regarding the determinants of its heterogeneous course. Machine learning techniques present an opportunity to develop tools to predict diagnosis and prognosis at an individual level. Methods We examined baseline (2008–2010) and follow-up (2012–2014) data of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), a large occupational cohort study. We implemented an elastic net regularization analysis with a 10-fold cross-validation procedure using socioeconomic and clinical factors as predictors to distinguish at follow-up: (1) depressed from non-depressed participants, (2) participants with incident depression from those who did not develop depression, and (3) participants with chronic (persistent or recurrent) depression from those without depression. Results We assessed 15 105 and 13 922 participants at waves 1 and 2, respectively. The elastic net regularization model distinguished outcome levels in the test dataset with an area under the curve of 0.79 (95% CI 0.76–0.82), 0.71 (95% CI 0.66–0.77), 0.90 (95% CI 0.86–0.95) for analyses 1, 2, and 3, respectively. Conclusions Diagnosis and prognosis related to depression can be predicted at an individual subject level by integrating low-cost variables, such as demographic and clinical data. Future studies should assess longer follow-up periods and combine biological predictors, such as genetics and blood biomarkers, to build more accurate tools to predict depression course.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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