Prediction of Hospital Readmission for Heart Disease: A Deep Learning Approach

2019 ◽  
pp. 16-26
Author(s):  
Jingwei Da ◽  
Danni Yan ◽  
Sijia Zhou ◽  
Yidi Liu ◽  
Xin Li ◽  
...  
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Hospital Readmission ◽  
Learning Approach

scholarly journals Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach

2018 ◽  
Author(s):  
Thanat Chokwijitkul ◽  
Anthony Nguyen ◽  
Hamed Hassanzadeh ◽  
Siegfried Perez
Keyword(s):  
Risk Factors ◽  
Heart Disease ◽  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Learning Approach

scholarly journals Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rohit Bharti ◽  
Aditya Khamparia ◽  
Mohammad Shabaz ◽  
Gaurav Dhiman ◽  
Sagar Pande ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Deep Learning ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Correct Prediction ◽  
Learning Approach ◽  
Multimedia Technology ◽  
Incorrect Prediction ◽  
Isolation Forest

The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Author response for "Deep learning and reinforcement learning approach on microgrid"

2020 ◽  
Author(s):  
Kumar Chandrasekaran ◽  
Prabaakaran Kandasamy ◽  
Srividhya Ramanathan
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Author Response ◽  
Learning Approach
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