scholarly journals PCP46 - A SIMPLE DECISION TREE TO IDENTIFY POTENTIAL APPLICATIONS OF MACHINE LEARNING AS AN ADDITION TO TRADITIONAL STATISTICAL ANALYSIS IN HEALTH ECONOMICS AND OUTCOMES RESEARCH

2018 ◽  
Vol 21 ◽  
pp. S88
Author(s):  
A. Cox ◽  
M. Oguz
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Health Economics ◽  
Decision Tree ◽  
Outcomes Research ◽  
Potential Applications ◽  
Applications Of Machine Learning

scholarly journals Machine Learning Based Statistical Analysis of Emotion Recognition using Facial Expression

2020 ◽  
Vol 11 (1) ◽  
pp. 1-8
Author(s):  
Aqib Ali ◽  
Jamal Abdul Nasir ◽  
Muhammad Munawar Ahmed ◽  
Samreen Naeem ◽  
Sania Anam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Decision Tree ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Digital Image ◽  
Search Algorithm ◽  
Statistical Feature ◽  
Tree Algorithms ◽  
Two Phases

Background: Humans can deliver many emotions during a conversation. Facial expressions show information about emotions. Objectives: This study proposed a Machine Learning (ML) approach based on a statistical analysis of emotion recognition using facial expression through a digital image. Methodology: A total of 600 digital image datasets divided into 6 classes (Anger, Happy, Fear, Surprise, Sad, and Normal) was collected from publicly available Taiwan Facial Expression Images Database. In the first step, all images are converted into a gray level format and 4 Regions of Interest (ROIs) are created on each image, so the total image dataset gets divided in 2400 (600 x 4) sub-images. In the second step, 3 types of statistical features named texture, histogram, and binary feature are extracted from each ROIs. The third step is a statistical feature optimization using the best-first search algorithm. Lastly, an optimized statistical feature dataset is deployed on various ML classifiers. Results: The analysis part was divided into two phases: firstly boosting algorithms-based ML classifiers (named as LogitBoost, AdaboostM1, and Stacking) which obtained 94.11%, 92.15%, and 89.21% accuracy, respectively. Secondly, decision tree algorithms named J48, Random Forest, and Random Committee were obtained with 97.05%, 93.14%, and 92.15% accuracy, respectively. Conclusion: It was observed that decision tree based J48 classifiers gave 97.05% classification accuracy.

scholarly journals CLINICAL APPLICATIONS OF MACHINE LEARNING ON COVID-19: THE USE OF A DECISION TREE ALGORITHM FOR THE ASSESSMENT OF PERCEIVED STRESS IN MEXICAN HEALTHCARE PROFESSIONALS

2020 ◽  
Author(s):  
Juan Luis Delgado-Gallegos ◽  
Gener Avilés-Rodriguez ◽  
Gerardo R. Padilla-Rivas ◽  
María De los Ángeles Cosio-León ◽  
Héctor Franco-Villareal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Perceived Stress ◽  
Healthcare Professionals ◽  
Statistical Correlation ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Managing Stress ◽  
Applications Of Machine Learning ◽  
C5.0 Decision Tree

AbstractStress and anxiety have shown to be indirect effects of the COVID-19 pandemic, therefore managing stress becomes essential. One of the most affected populations by the pandemic are healthcare professionals. Thus, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. In our study, we used a machine learning prediction model to help measure perceived stress; a C5.0 decision tree algorithm was used to analyze and classify datasets obtained from healthcare professionals of the northeast region of Mexico. Our analysis showed that 6 out of 102 instances were incorrectly classified. Missing two cases for mild, three for moderate and 1 for severe (accuracy of 94.1%), statistical correlation analysis was performed to ensure integrity of the method, in addition we concluded that severe stress cases can be related mostly to high levels of Xenophobia and Compulsive stress.

scholarly journals Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps

2020 ◽  
Vol 7 (1) ◽  
pp. 35-42
Author(s):  
Yixi Chen ◽  
Viktor V Chirikov ◽  
Xiaocong L Marston ◽  
Jingang Yang ◽  
Haibo Qiu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Economics ◽  
Outcomes Research ◽  
Economic Value ◽  
Care Utilization ◽  
Value Assessment ◽  
Clinical Value ◽  
Treatment Regimens ◽  
Precision Health

Precision health economics and outcomes research (P-HEOR) integrates economic and clinical value assessment by explicitly discovering distinct clinical and health care utilization phenotypes among patients. Through a conceptualized example, the objective of this review is to highlight the capabilities and limitations of machine learning (ML) applications to P-HEOR and to contextualize the potential opportunities and challenges for the wide adoption of ML for health economics. We outline a P-HEOR conceptual framework extending the ML methodology to comparatively assess the economic value of treatment regimens. Latest methodology developments on bias and confounding control in ML applications to precision medicine are also summarized.

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

Sign in / Sign up

Export Citation Format

Share Document