scholarly journals Heart Disease Classification Using Multiple K-PCA and Hybrid Deep Learning Approach

2022 ◽  
Vol 41 (3) ◽  
pp. 1273-1289
Author(s):  
S. Kusuma ◽  
Dr. Jothi K. R
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Disease Classification ◽  
Learning Approach

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 A Weighted Voting Deep Learning Approach for Plant Disease Classification

2021 ◽  
Vol 17 (12) ◽  
pp. 1172-1185
Author(s):  
Assia Ennouni ◽  
Noura Ouled Sihamman ◽  
My Abdelouahed Sabri ◽  
Abdellah Aarab
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Disease Classification ◽  
Learning Approach ◽  
Weighted Voting

Crop disease classification using deep learning approach: an overview and a case study

2020 ◽  
pp. 173-195
Author(s):  
Krishnaswamy Rangarajan Aravind ◽  
Prabhakar Maheswari ◽  
Purushothaman Raja ◽  
Cezary Szczepański
Keyword(s):  
Deep Learning ◽  
Disease Classification ◽  
Learning Approach ◽  
Crop Disease

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.

Potato Leaf Disease Classification Using Deep Learning Approach

2020 ◽  
Author(s):  
Rizqi Amaliatus Sholihati ◽  
Indra Adji Sulistijono ◽  
Anhar Risnumawan ◽  
Eny Kusumawati
Keyword(s):  
Deep Learning ◽  
Disease Classification ◽  
Learning Approach ◽  
Potato Leaf ◽  
Leaf Disease
Sign in / Sign up

Export Citation Format

Share Document