Performance Comparison of Three Classifiers for Fetal Health Classification Based on Cardiotocographic Data

Khare, Vijay; Kumari, Sakshi

doi:10.56578/ataiml010107

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…Skin detection is a critical but time-consuming task that necessitates careful consideration of colour spaces before proceeding. These studies employ a wide variety of colour spaces, from the familiar RGB model to the more experimental YUV and YCbCr colour spaces [25,26].…”

Section: Skin Detectionmentioning

confidence: 99%

Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning

Inamanamelluri,

Pulipati,

Pradhan

et al. 2023

RIA

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A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive.

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Section: Skin Detectionmentioning

confidence: 99%

Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning

Inamanamelluri,

Pulipati,

Pradhan

et al. 2023

RIA

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“…According to the study of Khare and Kumari [21], it was possible to calculate both the imbalance ratio and the average imbalance ratio to Eq. ( 1).…”

Section: Label Decoupling Modulementioning

confidence: 99%

“…The rate, at which the Mathematical Optimization Algorithm (MOA) operated, was determined using Eq. (21).…”

Section: Initial Stagementioning

confidence: 99%

Intelligent Diagnosis of Obstetric Diseases Using HGS-AOA Based Extreme Learning Machine

Vatambeti¹,

Damera²

2023

ATAIML

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This paper aimed to realize intelligent diagnosis of obstetric diseases using electronic medical records (EMRs). The Optimized Kernel Extreme Machine Learning (OKEML) technique was proposed to rebalance data. The hybrid approach of the Hunger Games Search (HGS) and the Arithmetic Optimization Algorithm (AOA) was adopted. This paper tested the effectiveness of the OKEML-HGS-AOA on Chinese Obstetric EMR (COEMR) datasets. Compared with other models, the proposed model outperformed the state-of-the-art experimental results on the COEMR, Arxiv Academic Paper Dataset (AAPD), and the Reuters Corpus Volume 1 (RCV1) datasets, with an accuracy of 88%, 90%, and 91%, respectively.

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“…In recent works, many people have proposed different classification algorithm [3,4]. For example, Praveenkumara et al [5] performed the support vector machine (SVM) model for fault identification from the acquired vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Classification Predictive Maintenance Using XGboost with Genetic Algorithm

Khiat¹,

Hebri²,

Besma³

2022

RIA

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This study develops a condition classification system of compressor 103J and water pump systems which are key equipment in the ammonia production line, hence the monitoring of these two very important machines. In recent years, there are many good intelligent machine learning algorithms and XGboost is one of them. However, it contains many parameters and classification performance of the model will be greatly affected by the selection of parameters and their combination technique. In this paper, XGboost algorithm is combined with the genetic algorithm, called GA-XGboost, in order to find the best hyper parameters of classifiers which makes the classifier more efficient and ensures the proper functioning of compressor 103J and water pump systems. Experiments show that GA-XGboost algorithm has improved the accuracy of classification in the compressor 103J and the water pump dataset compared with other machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF) and AdaBoost. Also experiments demonstrate the improvement of the GA-XGboost algorithm by the combination of different selection and crossover operators of the genetic algorithm.

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Performance Comparison of Three Classifiers for Fetal Health Classification Based on Cardiotocographic Data

Cited by 8 publications

References 13 publications

Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning

Classification of a New-Born Infant’s Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning

Intelligent Diagnosis of Obstetric Diseases Using HGS-AOA Based Extreme Learning Machine

Classification Predictive Maintenance Using XGboost with Genetic Algorithm

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