scholarly journals Artificial Neural Network and Its Application in Medical Disease Prediction: Review Article

2019 ◽  
Author(s):  
Putri Siswanto ◽  
Riries Rulaningtyas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Review Article ◽  
Disease Prediction ◽  
Medical Disease ◽  
Artificial Neural

An intelligent heart disease prediction system based on swarm-artificial neural network

2021 ◽  
Author(s):  
Sudarshan Nandy ◽  
Mainak Adhikari ◽  
Venki Balasubramanian ◽  
Varun G. Menon ◽  
Xingwang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Disease Prediction ◽  
Prediction System ◽  
Artificial Neural

Artificial Neural Network for Cardiovascular Disease Prediction

2021 ◽  
Vol 9 (8) ◽  
pp. 2189-2193
Author(s):  
Abhay Patil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Disease Classification ◽  
Disease Prediction ◽  
Intelligence Gathering ◽  
Binary Model ◽  
Cardio Vascular Disease ◽  
Artificial Neural ◽  
Cardio Vascular ◽  
The Ideal

Abstract: The assurance of coronary ailment a large part of the time depends upon an eccentric mix of clinical and masochist data. Considering this multifaceted nature, there exists a ton of income among clinical specialists and experts with respect to the useful and careful assumption for coronary sickness. In this paper, we cultivate a coronary disease prediction system that can help clinical specialists in expecting coronary ailment status reliant upon the clinical data of patients. Man-made intelligence-gathering strategies are amazingly useful in the clinical field by giving accurate results and quick finishes of ailments. Thusly, these techniques save part of the ideal opportunity for the two trained professionals and patients. The neural associations can be used as classifiers to expect the assurance of Cardiovascular Heart disorder. Keywords: Cardio Vascular disease, Classification, Artificial neural network, Categorical model and Binary model

scholarly journals Plant disease prediction using classification algorithms

2021 ◽  
Vol 10 (1) ◽  
pp. 257
Author(s):  
Maria Morgan ◽  
Carla Blank ◽  
Raed Seetan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nearest Neighbor ◽  
Disease Classification ◽  
Future Research ◽  
Support Vector ◽  
Classification Algorithms ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Artificial Neural

<p>This paper investigates the capability of six existing classification algorithms (Artificial Neural Network, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Decision Tree and Random Forest) in classifying and predicting diseases in soybean and mushroom datasets using datasets with numerical or categorical attributes. While many similar studies have been conducted on datasets of images to predict plant diseases, the main objective of this study is to suggest classification methods that can be used for disease classification and prediction in datasets that contain raw measurements instead of images. A fungus and a plant dataset, which had many differences, were chosen so that the findings in this paper could be applied to future research for disease prediction and classification in a variety of datasets which contain raw measurements. A key difference between the two datasets, other than one being a fungus and one being a plant, is that the mushroom dataset is balanced and only contained two classes while the soybean dataset is imbalanced and contained eighteen classes. All six algorithms performed well on the mushroom dataset, while the Artificial Neural Network and k-Nearest Neighbor algorithms performed best on the soybean dataset. The findings of this paper can be applied to future research on disease classification and prediction in a variety of dataset types such as fungi, plants, humans, and animals.</p>

Application of Artificial Neural Network for Cotton Boil Spoiling Disease Prediction

2010 ◽  
Vol 143-144 ◽  
pp. 233-237
Author(s):  
Fu Gui Chen ◽  
Bao Jian Zhang ◽  
Jun Hui Fu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Disease Prediction ◽  
Network System ◽  
Neural Network System ◽  
Average Quantity ◽  
Artificial Neural ◽  
High Output

Based on the database of cotton boil spoiling disease in Xinxiang, a computerized intelligent expert system was established by using the Reverse Model of artificial neural network. With its speediness, robustness and 100%predicting accuracy, the system can be used as an effective method to predict the trend of cotton diseases. In recent years, we have seem some reports for which use artificial neural network system to forecast the disease of crops, but the artificial neural network using for predicting cotton boil spoiling disease have not been seen yet. Xinxiang is a city of Henan province of china, according to the survey materials of 10 years, the high output cotton boil spoiling disease break out every 4 years, the average quantity is 1.53, the rate of boil spoiling disease is 11.84%, so the loss is 168.28 . In order to prevent the cotton boil spoiling disease, we should forecast the disease, by doing this, it can increase quantity and quality of the cotton.

scholarly journals Heart Disease Prediction Using Artificial Neural Network

IJARCCE ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 85-89
Author(s):  
Rachana Deshmukh ◽  
Payal Gourkhede ◽  
Sonali Rangari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Disease Prediction ◽  
Artificial Neural

scholarly journals Comparative Performance of Urine and Blood-based Attributes in Chronic Kidney Disease Prediction using Artificial Neural Network

2020 ◽  
Vol 17 (DEC 2020) ◽  
pp. 17-28
Author(s):  
M. H. A. Ghafar ◽  
Abdul Hadi A. Razak ◽  
M. S. A. Megat Ali ◽  
S. A. M. Al Junid ◽  
Adizul Ahmad ◽  
...  
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Artificial Neural Network ◽  
Kidney Disease ◽  
Disease Prediction ◽  
Comparative Performance ◽  
Artificial Neural
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