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>

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

A ONE-LAYER FEEDBACK ARTIFICIAL NEURAL NETWORK WITH A BAYESIAN LEARNING RULE

1989 ◽  
Vol 01 (01) ◽  
pp. 77-87 ◽  
Author(s):  
Anders Lansner ◽  
Örjan Ekeberg
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bayesian Learning ◽  
Rule Learning ◽  
Learning Rule ◽  
New Model ◽  
Content Addressable Memory ◽  
Binary Model ◽  
Artificial Neural ◽  
Associative Recall

A probabilistic artificial neural network is presented. It is of a one-layer, feedback-coupled type with graded units. The learning rule is derived from Bayes's rule. Learning is regarded as collecting statistics and recall as a statistical inference process. Units correspond to events and connections come out as compatibility coefficients in a logarithmic combination rule. The input to a unit via connections from other active units affects the a posteriori belief in the event in question. The new model is compared to an earlier binary model with respect to storage capacity, noise tolerance, etc. in a content addressable memory (CAM) task. The new model is a real time network and some results on the reaction time for associative recall are given. The scaling of learning and relaxation operations is considered together with issues related to representation of information in one-layer artificial neural networks. An extension with complex units is discussed.

Sign in / Sign up

Export Citation Format

Share Document