The Modeling of Artificial Neural Network of Early Diagnosis for Malnutrition with Backpropagation Method

Hayat, Cynthia; Abian, Barens

doi:10.1109/iac.2018.8780505

Cited by 6 publications

(6 citation statements)

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“…Exploration of the feasibilities of direct and integrative interventions could potentially contribute to informing policy making for the achievement of key global goals. To date, studies using AI to analyze data related to malnutrition, such as those conducted by Hayat and Abian [39], and Neill [40], have required analysts to possess advanced knowledge in AI, mathematics, or computer programming, which might be too technical for laypersons who are not trained in computer science. Malnutrition researchers might have wished that they too, could use AI to generate predictive simulations of alternative counterfactual scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…Malnutrition and public healthcare researchers, such as Hayat and Abian [39] and Neill [40] have utilized AI in their studies. However, the AI techniques outlined in those studies might be too technically difficult for analysts and stakeholders who may not be trained in artificial intelligence or computer science to understand.…”

Section: Rationale For Using the Ai-based Bayesian Network Approachmentioning

confidence: 99%

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Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach

How

Chan

2020

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According to the World Health Organization (WHO) and the World Bank, malnutrition is one of the most serious but least-addressed development challenges in the world. Malnutrition refers to the malfunction or imbalance of nutrition, which could be influenced not only by under-nourishment, but also by over-nourishment. The significance of this paper is that it shows how artificial intelligence (AI) can be democratized to enable analysts who are not trained in computer science to also use human-centric explainable-AI to simulate the possible dynamics between malnutrition, health and population indicators in a dataset collected from 180 countries by the World Bank. This AI-based human-centric probabilistic reasoning approach can also be used as a cognitive scaffold to educe (draw out) AI-Thinking in analysts to ask further questions and gain deeper insights. In this study, a rudimentary beginner-friendly AI-based Bayesian predictive modeling approach was used to demonstrate how human-centric probabilistic reasoning could be utilized to analyze the dynamics of global malnutrition and optimize conditions for achieving the best-case scenario. Conditions of the worst-case “Black Swan” scenario were also simulated, and they could be used to inform stakeholders to prevent them from happening. Thus, the nutritional and health status of vulnerable populations could be ameliorated.

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Section: Discussionmentioning

confidence: 99%

Section: Rationale For Using the Ai-based Bayesian Network Approachmentioning

confidence: 99%

Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach

How

Chan

2020

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“…Artificial neural network adopts the working principle of the human brain's nervous system in learning, remembering information, and determining patterns. The artificial neural network approach itself has been widely used in the medical field, including for early identification and diagnosis, as well as can reduce manual diagnostic errors because the system can combine the experience and knowledge of several doctors [12], [13]. The work emphasis of the artificial neural network algorithm is focused on building a network to be able to make good predictions.…”

Section: Related Workmentioning

confidence: 99%

“…Hayat & Soenandi Journal of Information Systems Engineering and Business Intelligence, 2024, 10 (1),[1][2][3][4][5][6][7][8][9][10][11][12] …”

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confidence: 99%

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Hayat,

Soenandi

2024

J. Inf. Syst. Eng. Bus. Intell.

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Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization

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“…Dependence on the knowledge of an expert radiologist regarding the principles of anatomy, physiology, and pathology is a factor that can hinder making a diagnosis as early as possible [5]. Another difficulty in the process of detecting CXR images is the difficulty of expert radiologists in developing consistent reasoning techniques in reading CXR images while considering all common chest diseases that require a long time to diagnose a CXR image [6].…”

Section: Introductionmentioning

confidence: 99%

DenseNet-CNN Architectural Model for Detection of Abnormality in Acute Pulmonary Edema

Hayat

2021

khif

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Acute pulmonary edema (EPA) is a condition of emergency respiratory distress that results from the sudden and rapid build-up of fluid into the lungs. Rapid screening of EPA patients is necessary so that radiologists can make the prognosis as early as possible. In addition, reliance on the expert's knowledge of reasoning also hinders the diagnostic process. This research was conducted by developing an architectural model for machine learning systems with a deep learning approach. With the concept of representative learning, the denseNet-CNN algorithm connects each layer to another by means of a feed-forward. The data used is Image CXR-14 specifically labeled pulmonary edema pathology. The size of each CXR-14 image is 1024 × 1024 with a value of 8 bits grayscale. The size of each CXR-14 image is 1024 × 1024 with a value of 8 bits grayscale. The architectural model development stages consist of the preparation stage, data resampling, data training and data testing. Optimizer parameters used are Adam's optimizer, learning rate of 0.0001 and weight decay = 1e-5 and the loss used is binary cross entropy. The resulting mean AUROC analysis showed the sensitivity value of the 10% dataset was 71.493% and the specificity value of 10.011% was obtained at the second hold of the k-fold cross validation method after holdout validation, so that the resulting model was valid. The detection system developed from the denseNet-CNN model is expected to help radiologists identify abnormalities in CXR images quickly, precisely, and consistently. The denseNet-CNN model is also developed in the form of a heatmap visualization by localizing the features you are looking for. With localization in the form of a heat map, detection of pathological abnormalities of PEA is easier to do and to be recognized.

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The Modeling of Artificial Neural Network of Early Diagnosis for Malnutrition with Backpropagation Method

Cited by 6 publications

References 10 publications

Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach

Artificial Intelligence-Enabled Predictive Insights for Ameliorating Global Malnutrition: A Human-Centric AI-Thinking Approach

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

DenseNet-CNN Architectural Model for Detection of Abnormality in Acute Pulmonary Edema

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