Implementation of Backpropagation Neural Network to Detect Suspected Lung Disease

Syafria, Fadhilah; Iqbal, Boni; Budianita, Elvia; Afrianty, Iis

doi:10.24014/ijaidm.v1i1.5023

Cited by 5 publications

(4 citation statements)

References 1 publication

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“…While in [6], there are 46 symptoms and 36 rules obtained from the expert and represented by decision table. With the identical architecture (i.e., backpropagation), as stated in [8], we found out the difference acquired accuracy of the system. The system achieved an accuracy of 82 % at both variations of distribution data (training and testing set), namely 90:10 and 80:20.…”

Section: Testing Phasementioning

confidence: 98%

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Identification of fat-soluble vitamins deficiency using artificial neural network

Sagala

Hayat

Tandipuang

2019

Jurnal Teknologi dan Sistem Komputer

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The fat-soluble vitamins (A, D, E, K) deficiency remain frequent universally and may have consequential adverse resultants and causing slow appearance symptoms gradually and intensify over time. The vitamin deficiency detection requires an experienced physician to notice the symptoms and to review a blood test’s result (high-priced). This research aims to create an early detection system of fat-soluble vitamin deficiency using artificial neural network Back-propagation. The method was implemented by converting deficiency symptoms data into training data to be used to produce a weight of ANN and testing data. We employed Gradient Descent and Logsig as an activation function. The distribution of training data and test data was 71 and 30, respectively. The best architecture generated an accuracy of 95 % in a combination of parameters using 150 hidden layers, 10000 epoch, error target 0.0001, learning rate 0.25.

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Section: Testing Phasementioning

confidence: 98%

“…Sevani and Joshua [7] studied identification fat-soluble vitamin deficiency using forward chaining. Sayfria et al [8] proposed the Backpropagation Neural Network (BPNN) to detect suspected person contracted lung disease. Khan et al [9] performed BPNN to detect tuberculosis suspected patients for the disease early management.…”

Section: Introductionmentioning

confidence: 99%

Identification of fat-soluble vitamins deficiency using artificial neural network

Sagala

Hayat

Tandipuang

2019

Jurnal Teknologi dan Sistem Komputer

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“…Kata kunci: COVID-19, Chest X-Ray, Backpropagation, Pandemi, Algoritma. [2], memprediksi dan pengenalan pola [3] dan juga mendeteksi dugaan penyakit paru berdasarkan gejala [4]. Selain itu, algoritma jaringan syaraft tiruan Bacpropagation juga sering digunakan dalam pengklasifikasian dan identifikasi citra.…”

Section: Abstrakunclassified

Identifikasi Penderita COVID-19 Berdasarkan Chest X-Ray Menggunakan Algoritma Jaringan Syaraf Tiruan Backpropagation

Putra

Yuhandri

2021

JSisfotek

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Corona Virus Disease 2019 (COVID-19) is an infectious respiratory disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV2). This disease first appeared in Wuhan, China and spread throughout the world. COVID-19 has had a major impact on public health around the world. On March 9, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. Early identification of people with COVID-19 can help limit the wider spread. One of the factors behind the rapid spread of the disease is the long clinical trial time. Rapid clinical testing is a challenge facing the spread of COVID-19. Most countries, including Indonesia, face the problem of lack of detection equipment and experts in diagnosing this disease. Chest X-Ray is one of the medical imaging techniques and also an alternative to identify the symptoms of pneumonia caused by COVID-19. This study aims to identify pneumonia caused by COVID-19 and other diseases based on Chest X-Ray. 107 Chest X-Ray images used as material for this study were obtained from the General Hospital of Ibnu Sina Padang Indonesia, which consisted of 27 images of pneumonia caused by COVID-19, 51 images with other diseases and 29 images of normal lungs. Then pre-processing is carried out as an initial stage and then feature extraction is carried out. Furthermore, the learning and identification process is carried out using the Backpropagation Artificial Neural Network (ANN) algorithm. In this study, 92 images were used as training data, and 15 images were used as test data. The results of calculations carried out using a network with a pattern of 16-100-100-100-2 obtained an accuracy value of 73%. The results of the identification prediction can be used as consideration in establishing a diagnosis of COVID-19 sufferers, but cannot be used as an absolute reference.

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“…Artificial neural networks have advantages over other conventional classification methods, which are more reliable against noise in the data (Wibawa & Maysanjaya, 2018). In addition, artificial neural networks also have the ability to produce good predictions to solve a problem despite using a limited number of samples (Panca Saputra & Panca, 2020). Artificial neural networks have been widely applied in various fields for pattern recognition, pattern classification and pattern prediction (Selwal & Raoof, 2020).…”

Section: Introductionmentioning

confidence: 99%

Stunting Disease Classification Using Multi-Layer Perceptron Algorithm with GridSearchCV

Cahyani,

Ashuri,

Aditya

2024

SinkrOn

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Stunting is a growth and development disorder caused by malnutrition characterized by a child's height less than the standard deviation set by WHO. In 2022, stunting cases in Indonesia are considered a high prevalence rate, reaching 21.6%. There are several factors that can cause stunting in children, namely maternal and antenatal care factors, home environment factors, breastfeeding practices, and feeding factors during toddlerhood. There are several impacts that occur when children are stunted, namely increased risk of child mortality, susceptibility to illness, impaired brain development, physical disorders and metabolic disorders. Currently, deep learning has been widely used for disease classification and prediction, one of the deep learning methods is Multi-Layer Perceptron (MLP). The purpose of this research is to classify stunting disease using a deep learning method, namely MLP. The dataset used consists of 8 attributes, namely gender, age, birth weight, birth length, body weight, body length, breastfeeding and stunting with a total of 10,000 records. The encoding process is carried out to convert categorical data into numeric attributes of gender, breastfeeding, and stunting. This research produces a higher accuracy value than previous research which used the C4.5 algorithm with an accuracy of 61.82%, whereas in this study using MLP which was integrated with the GridSearchCV hyperparameter it obtained an accuracy of 82.37%. This proves that the MLP method is successful in classifying stunting compared to previous research algorithms.

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Implementation of Backpropagation Neural Network to Detect Suspected Lung Disease

Cited by 5 publications

References 1 publication

Identification of fat-soluble vitamins deficiency using artificial neural network

Identification of fat-soluble vitamins deficiency using artificial neural network

Identifikasi Penderita COVID-19 Berdasarkan Chest X-Ray Menggunakan Algoritma Jaringan Syaraf Tiruan Backpropagation

Stunting Disease Classification Using Multi-Layer Perceptron Algorithm with GridSearchCV

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