Functional Testing on Heart Disease Detection Using Multilayer Perceptron Technique

The proposed work shows how important and useful testing for any machine learning application and need to make better models such that bugs and errors are minimized. The proposed work considers patient’s health data which can be used for decision making or prediction using various calculation, in this work, developing a heart condition prediction system mainly concentrating on artificial neural network, which uses the multilayer perceptron algorithm for the execution. Our dataset consists of vital information about the patient such as age, gender, blood pressure, ECG measures, and stroke history. This labeled dataset predicts the probability of a patient to have heart diseases or not. To fulfill the proposed work, created a GUI to get the information about a new patient. Once the development of the model finished, complete functionality testing was applied. The report of the testing helped in identifying the bugs and errors which on rectification by the developer helped in increasing the accuracy of the artificial neural network.

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

Neural Computing and Applications ◽

10.1007/s00521-021-06124-1 ◽

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

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Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽

10.3390/math8050766 ◽

2020 ◽

Vol 8 (5) ◽

pp. 766

Author(s):

Rashad A. R. Bantan ◽

Ramadan A. Zeineldin ◽

Farrukh Jamal ◽

Christophe Chesneau

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Multilayer Perceptron ◽

Research Study ◽

Real Data ◽

Factors Affecting ◽

King Abdulaziz University ◽

Artificial Neural ◽

Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

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Abstract P365: Blood Pressure Evaluation From Ppg Signal Analysis and Artificial Neural Network

Hypertension ◽

10.1161/hyp.70.suppl_1.p365 ◽

2017 ◽

Vol 70 (suppl_1) ◽

Author(s):

Francesco Lamonaca ◽

Vitaliano Spagnuolo ◽

Serena De Prisco ◽

Domenico L Carnì ◽

Domenico Grimaldi

Keyword(s):

Neural Network ◽

Blood Pressure ◽

Artificial Neural Network ◽

Audio Signal ◽

Hardware Interface ◽

Validation Procedure ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The Time Domain ◽

And Storage

The analysis of the PPG signal in the time domain for the evaluation of the blood pressure (BP) is proposed. Some features extracted from the PPG signal are used to train an Artificial Neural Network (ANN) to determine the function that fit the target systolic and diastolic BP. The data related to the PPG signals and BP used in the analysis are provided by the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC II) database. The pre-analysis of the signal to remove inconsistent data is also proposed. A set of 1750 valid pulse is considered. The 80% of the input samples is used for the training of the network. Instead, the 10% of the input data are used for the validation of the network and 10% for final test of this last. The results show as the error for both the systolic and diastolic BP evaluation is included in the range of ±3 mmHg. Tab.1 shows the results for 20 PPG pulses randomly selected analyzed together with the systolic and diastolic blood pressure furnished by MIMC and evaluated by the trained ANN. Tab.1 experimental results comparing MIMIC and the ANN results. Moreover, a suitable hardware to validate the ANN with the sphygmomanometer is designed and realized. This hardware allows clinicians to collect data according to the requirements of the validation procedure. With the sphygmomanometer the systolic and diastolic values are referred to two different PPG pulses. As a consequence, it is proposed a new hardware interface allowing the synchronized acquisition and storage of the PPG signal and clinician voice. For the validation, the clinician: (i) evaluates the BP on both the arms and assesses that no significant differences occur; (ii) plugs the PPG sensor on the finger of one arm; (iii) starts the recording of both the PPG signal and the audio signal; (iv) evaluates the BP on the other arm with sphygmomanometer and says the systolic and diastolic values when detected. Through suitable post processing algorithm, the Systolic and Diastolic values are associated to the corresponding PPG Pulses. Following this procedure, the dataset to further validate the ANN according the standard is obtained. Once the ANN is validated it will be implemented on smartphone to have always in the pocket a reliable measurement system for Blood Pressure, oximetry and heart rate.

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Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2021070103 ◽

2021 ◽

Vol 12 (3) ◽

pp. 35-43

Author(s):

Pratibha Verma ◽

Vineet Kumar Awasthi ◽

Sanat Kumar Sahu

Keyword(s):

Neural Network ◽

Coronary Artery Disease ◽

Artificial Neural Network ◽

Coronary Artery ◽

Multilayer Perceptron ◽

Artificial Neural ◽

Artery Disease ◽

Artificial Neural Network Ann ◽

Hidden Layer

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

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Other Diseases Detection

Medical Diagnosis Using Artificial Neural Networks ◽

10.4018/978-1-4666-6146-2.ch012 ◽

2014 ◽

pp. 170-180

Keyword(s):

Neural Network ◽

Decision Making ◽

Artificial Neural Network ◽

Multilayer Perceptron ◽

Medical Diagnosis ◽

Intelligent System ◽

Associated Symptoms ◽

Artificial Neural ◽

Inputs And Outputs

Considering the importance of the problem of medical diagnosis, this chapter investigates the application of an intelligent system based on artificial neural network for decision making for Hepatitis. First, datasets are provided for detecting Hepatitis, based on the requirements of artificial neural network inputs and outputs consisting of associated symptoms of each disease as fields of patients' records. Then multilayer perceptron (MLP) artificial neural network is trained to classify Hepatitis disease. In the next sections, details are described.

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