This document discusses advancements in machine learning and predictive analytics for healthcare. It begins with an introduction discussing how technologies like machine learning and artificial intelligence can help researchers and doctors achieve goals faster when integrated with healthcare. The document then reviews literature on challenges with analyzing big healthcare data due to issues like data variety, speed and volume. It discusses different machine learning algorithms that have been used for disease prediction and diagnosis, including decision trees, random forests, bagging and boosting. The methodology section outlines the use of an ensemble approach, combining multiple models to improve overall accuracy. Technologies implemented in this work include Python libraries like Pandas, NumPy and Scikit-learn for data processing and modeling, along with Flask and AWS for web app deployment. The
A comprehensive study on disease risk predictions in machine learning IJECEIAES
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
The core of artificial intelligence is certification. It demonstrates a productive method for resolving urgent issues, and it denotes how to approach a dataset. For speedier access and seamless client service, we integrate artificial intelligence into our concept. Tensor-flow is used in its development to accelerate activities and automate data collection. Our module is standardised using a standard scaler. Cross-validation is done using the grid-search CV approach. The random-forest algorithm is the algorithm that we defined here. We reduce processing time while increasing usability adaptability. We criticise the strong emphasis on current solutions when it comes to attribution as a process for knowledge development since this focus influences the knowledge structure. Like chronic illness, which takes a lengthy time to diagnose because it is a long-lasting sickness, everywhere on the globe, chronic diseases are dangerous illnesses that are more expensive to detect and force the patient to endure their effects for the rest of their lives. There is a wealth of information about these diseases in the medical field; thus, data mining techniques are used to simplify the healthcare system.
ARTICLEAnalysing the power of deep learning techniques ovedessiechisomjj4
ARTICLE
Analysing the power of deep learning techniques over the
traditional methods using medicare utilisation and provider data
Varadraj P. Gurupura, Shrirang A. Kulkarnib, Xinliang Liua, Usha Desai c and Ayan Nasird
aDepartment of Health Management and Informatics, University of Central Florida, Orlando, FL, USA; bSchool of
Computer Science and Engineering, Vellore Institute of Technology, Vellore, India; cDepartment of Electronics and
Communication Engineering, Nitte Mahalinga Adyanthaya Memorial Institute of Technology, Nitte, Udupi, India;
dUCF School of Medicine, University of Central Florida, Orlando, FL, USA
ABSTRACT
Deep Learning Technique (DLT) is the sub-branch of Machine
Learning (ML) which assists to learn the data in multiple levels of
representation and abstraction and shows impressive performance
on many Artificial Intelligence (AI) tasks. This paper presents a new
method to analyse the healthcare data using DLT algorithms and
associated mathematical formulations. In this study, we have first
developed a DLT to programme two types of deep learning neural
networks, namely: (a) a two-hidden layer network, and (b) a three-
hidden layer network. The data was analysed for predictability in
both of these networks. Additionally, a comparison was also made
with simple and multiple Linear Regression (LR). The demonstration
of successful application of this method is carried out using the
dataset that was constructed based on 2014 Medicare Provider
Utilization and Payment Data. The results indicate a stronger case
to use DLTs compared to traditional techniques like LR. Furthermore,
it was identified that adding more hidden layers to neural network
constructed for performing deep learning analysis did not have
much impact on predictability for the dataset considered in this
study. Therefore, the experimentation described in this article sets
up a case for using DLTs over the traditional predictive analytics. The
investigators assume that the algorithms described for deep learning
is repeatable and can be applied for other types of predictive ana-
lysis on healthcare data. The observed results indicate, the accuracy
obtained by DLT was 40% more accurate than the traditional multi-
variate LR analysis.
ARTICLE HISTORY
Received 16 April 2018
Accepted 30 August 2018
KEYWORDS
Deep Learning Technique
(DLT); medicare data;
Machine Learning (ML);
Linear Regression (LR);
Confusion Matrix (CM)
Introduction
Methods involving Artificial Intelligence (AI) associated with Deep Learning Technique (DLT)
and Machine Learning (ML) are slowly but surely being used in medical and health infor-
matics. Traditionally, techniques such as Linear Regression (LR) (Nimon & Oeswald, 2013),
Analysis of Variance (ANOVA) (Kim, 2014), and Multivariate Analysis of Variance (MANOVA)
(Xu, 2014) (Malehi et al., 2015) have been used for predicting outcomes in healthcare.
However, in the recent years the methods of analysis applied are changing towards the
aforementi ...
A comprehensive study on disease risk predictions in machine learning IJECEIAES
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
The core of artificial intelligence is certification. It demonstrates a productive method for resolving urgent issues, and it denotes how to approach a dataset. For speedier access and seamless client service, we integrate artificial intelligence into our concept. Tensor-flow is used in its development to accelerate activities and automate data collection. Our module is standardised using a standard scaler. Cross-validation is done using the grid-search CV approach. The random-forest algorithm is the algorithm that we defined here. We reduce processing time while increasing usability adaptability. We criticise the strong emphasis on current solutions when it comes to attribution as a process for knowledge development since this focus influences the knowledge structure. Like chronic illness, which takes a lengthy time to diagnose because it is a long-lasting sickness, everywhere on the globe, chronic diseases are dangerous illnesses that are more expensive to detect and force the patient to endure their effects for the rest of their lives. There is a wealth of information about these diseases in the medical field; thus, data mining techniques are used to simplify the healthcare system.
ARTICLEAnalysing the power of deep learning techniques ovedessiechisomjj4
ARTICLE
Analysing the power of deep learning techniques over the
traditional methods using medicare utilisation and provider data
Varadraj P. Gurupura, Shrirang A. Kulkarnib, Xinliang Liua, Usha Desai c and Ayan Nasird
aDepartment of Health Management and Informatics, University of Central Florida, Orlando, FL, USA; bSchool of
Computer Science and Engineering, Vellore Institute of Technology, Vellore, India; cDepartment of Electronics and
Communication Engineering, Nitte Mahalinga Adyanthaya Memorial Institute of Technology, Nitte, Udupi, India;
dUCF School of Medicine, University of Central Florida, Orlando, FL, USA
ABSTRACT
Deep Learning Technique (DLT) is the sub-branch of Machine
Learning (ML) which assists to learn the data in multiple levels of
representation and abstraction and shows impressive performance
on many Artificial Intelligence (AI) tasks. This paper presents a new
method to analyse the healthcare data using DLT algorithms and
associated mathematical formulations. In this study, we have first
developed a DLT to programme two types of deep learning neural
networks, namely: (a) a two-hidden layer network, and (b) a three-
hidden layer network. The data was analysed for predictability in
both of these networks. Additionally, a comparison was also made
with simple and multiple Linear Regression (LR). The demonstration
of successful application of this method is carried out using the
dataset that was constructed based on 2014 Medicare Provider
Utilization and Payment Data. The results indicate a stronger case
to use DLTs compared to traditional techniques like LR. Furthermore,
it was identified that adding more hidden layers to neural network
constructed for performing deep learning analysis did not have
much impact on predictability for the dataset considered in this
study. Therefore, the experimentation described in this article sets
up a case for using DLTs over the traditional predictive analytics. The
investigators assume that the algorithms described for deep learning
is repeatable and can be applied for other types of predictive ana-
lysis on healthcare data. The observed results indicate, the accuracy
obtained by DLT was 40% more accurate than the traditional multi-
variate LR analysis.
ARTICLE HISTORY
Received 16 April 2018
Accepted 30 August 2018
KEYWORDS
Deep Learning Technique
(DLT); medicare data;
Machine Learning (ML);
Linear Regression (LR);
Confusion Matrix (CM)
Introduction
Methods involving Artificial Intelligence (AI) associated with Deep Learning Technique (DLT)
and Machine Learning (ML) are slowly but surely being used in medical and health infor-
matics. Traditionally, techniques such as Linear Regression (LR) (Nimon & Oeswald, 2013),
Analysis of Variance (ANOVA) (Kim, 2014), and Multivariate Analysis of Variance (MANOVA)
(Xu, 2014) (Malehi et al., 2015) have been used for predicting outcomes in healthcare.
However, in the recent years the methods of analysis applied are changing towards the
aforementi ...
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
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• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
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• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
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• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
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