scholarly journals A decision support system for primary headache developed through machine learning

PeerJ ◽  
2022 ◽  
Vol 10 ◽  
pp. e12743
Author(s):  
Fangfang Liu ◽  
Guanshui Bao ◽  
Mengxia Yan ◽  
Guiming Lin
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Support ◽  
Statistical Analysis ◽  
Diagnostic Accuracy ◽  
Confusion Matrix ◽  
Primary Headache ◽  
Primary Headaches ◽  
Decision Making System ◽  
Tension Type Headaches

Background Primary headache is a disorder with a high incidence and low diagnostic accuracy; the incidence of migraine and tension-type headache ranks first among primary headaches. Artificial intelligence (AI) decision support systems have shown great potential in the medical field. Therefore, we attempt to use machine learning to build a clinical decision-making system for primary headaches. Methods The demographic data and headache characteristics of 173 patients were collected by questionnaires. Decision tree, random forest, gradient boosting algorithm and support vector machine (SVM) models were used to construct a discriminant model and a confusion matrix was used to calculate the evaluation indicators of the models. Furthermore, we have carried out feature selection through univariate statistical analysis and machine learning. Results In the models, the accuracy, F1 score were calculated through the confusion matrix. The logistic regression model has the best discrimination effect, with the accuracy reaching 0.84 and the area under the ROC curve also being the largest at 0.90. Furthermore, we identified the most important factors for distinguishing the two disorders through statistical analysis and machine learning: nausea/vomiting and photophobia/phonophobia. These two factors represent potential independent factors for the identification of migraines and tension-type headaches, with the accuracy reaching 0.74 and the area under the ROC curve being at 0.74. Conclusions Applying machine learning to the decision-making system for primary headaches can achieve a high diagnostic accuracy. Among them, the discrimination effect obtained by the integrated algorithm is significantly better than that of a single learner. Second, nausea/vomiting, photophobia/phonophobia may be the most important factors for distinguishing migraine from tension-type headaches.

Automatic diagnosis of primary headaches by machine learning methods

Open Medicine ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. 157-165 ◽  
Author(s):  
Bartosz Krawczyk ◽  
Dragan Simić ◽  
Svetlana Simić ◽  
Michał Woźniak
Keyword(s):  
Machine Learning ◽  
Negative Impact ◽  
Life Quality ◽  
Primary Headache ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Common Disease ◽  
Primary Headaches ◽  
Starting Point ◽  
Headache Diagnosis

AbstractPrimary headaches are common disease of the modern society and it has high negative impact on the productivity and the life quality of the affected person. Unfortunately, the precise diagnosis of the headache type is hard and usually imprecise, thus methods of headache diagnosis are still the focus of intense research. The paper introduces the problem of the primary headache diagnosis and presents its current taxonomy. The considered problem is simplified into the three class classification task which is solved using advanced machine learning techniques. Experiments, carried out on the large dataset collected by authors, confirmed that computer decision support systems can achieve high recognition accuracy and therefore be a useful tool in an everyday physician practice. This is the starting point for the future research on automation of the primary headache diagnosis.

scholarly journals Intelligent Predicting Learning Disabilities in School Going Children using Fuzzy Logic K Mean Clustering in Machine Learning.

2019 ◽  
Vol 8 (4) ◽  
pp. 1694-1698
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Decision Making ◽  
Learning Disabilities ◽  
Fuzzy Logic ◽  
Side Effects ◽  
Early Analysis ◽  
Comprehensive Investigation ◽  
Engineering Statistics ◽  
Decision Making System

Learning disabilities (LD) is turning into a major issue in various nations around the globe which can even contrarily influence human common advancement. The undertaking of this work is to help the specialized programme network in their task to be with the standard. The underlying section of the paper gives a comprehensive investigation of the distinctive components of diagnosing learning disabilities. Despite the fact that LD can be analysed early - before 5 years of age, most youngsters were not determined to have LD until the age of nine on account of its unpredictable side effects and unclear indication in children disorder issue. Fuzzy logic K-means clustering has inspired a tremendous transformation in Machine learning and can take and able to resolve a variation of problems. This paper is the elaboration on the strategy for utilizing this mix to encourage the early analysis of LD. Since Fuzzy Logic clustering in Machine Learning is generally considered and connected in different areas of science, we invite all the related analysts from the fields of computer science, engineering, statistics, social sciences, healthcare, and so on, etc. The result of the paper demonstrates that the previously mentioned methodology can possibly be the potential of the supporting decision-making system in LD investigating and diagnosing.

Computerised Decision Support for Women’s Health Informatics

2012 ◽  
pp. 1404-1416 ◽  
Author(s):  
David Parry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Analysis ◽  
Health Informatics ◽  
Learning Approaches ◽  
Learning From Data ◽  
Computerized Records

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

Headaches

2021 ◽  
pp. 329-342
Author(s):  
Andrew D. Hershey
Keyword(s):  
Early Recognition ◽  
Primary Headache ◽  
Primary Care Providers ◽  
School Nurses ◽  
Primary Headaches ◽  
Care Providers ◽  
Primary Headache Disorders ◽  
Tension Type Headaches ◽  
The Impact

This chapter discusses recurrent headaches, especially when episodic, which are much more likely to represent primary headache disorders. Primary headaches are intrinsic to the nervous system and are the disease itself. Early recognition of the primary headaches in patients should result in improved response and outcome, minimizing the impact of the primary headaches and disability. Primary headaches can be grouped into migraine, tension-type headaches, and trigeminal autonomic cephalalgia, and an additional grouping of rarer headaches without a secondary cause. The primary headache that has the greatest impact on a child’s quality of life and disability is migraine, and subsequently is the most frequent primary headache brought to the attention of parents, primary care providers, and school nurses.

Computerised Decision Support for Women's Health Informatics

2009 ◽  
pp. 302-314
Author(s):  
David Parry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Analysis ◽  
Health Informatics ◽  
Learning Approaches ◽  
Learning From Data ◽  
Computerized Records

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

An Assessment of Federated Machine Learning for Translational Research

2020 ◽  
pp. 123-142
Author(s):  
Manoj A. Thomas ◽  
Diya Suzanne Abraham ◽  
Dapeng Liu
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Support ◽  
Translational Research ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
High Quality ◽  
Intermediate Data ◽  
Translational Informatics ◽  
Data Analytic

Translational research (TR) is the harnessing of knowledge from basic science and clinical research to advance healthcare. As a sister discipline, translational informatics (TI) concerns the application of informatics theories, methods, and frameworks to TR. This chapter builds upon TR concepts and aims to advance the use of machine learning (ML) and data analytics for improving clinical decision support. A federated machine learning (FML) architecture is described to aggregate multiple ML endpoints, and intermediate data analytic processes and products to output high quality knowledge discovery and decision making. The proposed architecture is evaluated for its operational performance based on three propositions, and a case for clinical decision support in the prediction of adult Sepsis is presented. The chapter illustrates contributions to the advancement of FML and TI.

scholarly journals TOPSIS Method Application for Decision Support System in Internal Control for Selecting Best Employees

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Robbi Rahim ◽  
Tri Listyorini
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Internal Control ◽  
Computational Method ◽  
Top Management ◽  
Job Responsibilities ◽  
Decision Making System ◽  
Selection Of

The selection of the best employees is one of the process of evaluating how well the performance of the employees is adjusted to the standards set by the company and usually done by top management such as General Manager or Director. In general, the selection of the best employees is still perform manually with many criteria and alternatives, and this usually make it difficult top managerial making decisions as well as the selection of the best employees periodically into a long and complicated process. Therefore, it is necessary to build a decision support system that can help facilitate the decision maker in determining the best choice based on standard criteria, faster, and more objective. In this research, the computational method of decision-making system used is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The criteria used in the selection of the best employees are: job responsibilities, work discipline, work quality, and behaviour. The final result of the global priority value of the best employee candidates is used as the best employee selection decision making tool by top management.

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