Comparative Study of Different Classification Algorithms on ILPD Dataset to Predict Liver Disorder

Pathan, Ayesha

doi:10.22214/ijraset.2018.2056

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…In terms of the analysis methods, we used both new and traditional supervised analysis methods, such as machine learning algorithms and multivariate regression methods, to predict the childhood outcomes of ARI disease symptoms. Furthermore, these findings complement other comparative machine learning findings [38][39][40][41] in providing evidence of the better performance of the random forest algorithm (88.7%) than traditional methods of analysis. However, other studies [42,43] contradicted these findings.…”

Section: Discussionsupporting

confidence: 81%

Analysis of risk factors associated with acute respiratory infections among under-five children in Uganda

Nshimiyimana

Zhou

2022

BMC Public Health

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Background Globally, infectious diseases are the major cause of death in children under the age of 5 years. Sub-Saharan Africa and South Asia account for 95% of global child mortalities every year, where acute respiratory infections (ARI) remain the leading cause of child morbidity and mortality. The aim of this study is to analyze the risk factors of ARI disease symptoms among children under the age of 5 years in Uganda. Methods A cross-sectional design was used to analyze 2016 Uganda Demographic and Health Survey (UDHS) data collected on 13,493 children under the age of 5 years in Uganda. Various methods, such as logistic regression, elastic net logistic regression, decision tree, and random forest, were compared and used to predict 75% of the symptom outcomes of ARI disease. Well-performing methods were used to determine potential risk factors for ARI disease symptoms among children under the age of 5 years. Results In Uganda, about 40.3% of children were reported to have ARI disease symptoms in the 2 weeks preceding the survey. Children under the age of 24 months were found to have a high prevalence of ARI disease symptoms. By considering 75% of the sample, the random forest was found to be a well-performing method (accuracy = 88.7%; AUC = 0.951) compared to the logistic regression method (accuracy = 62.0%; AUC = 0.638) and other methods in predicting childhood ARI symptoms. In addition, one-year old children (OR: 1.27; 95% CI: 1.12–1.44), children whose mothers were teenagers (OR: 1.28; 95% CI: 1.06–1.53), and farm workers (1.25; 95% CI: 1.11–1.42) were most likely to have ARI disease symptoms than other categories. Furthermore, children aged 48–59 months (OR: 0.69; 95% CI: 0.60–0.80), breastfed children (OR: 0.83; 95% CI: 0.76–0.92), usage of charcoal in cooking (OR: 0.77; 95% CI: 0.69–0.87), and the rainy season effect (OR: 0.66; 95% CI: 0.61–0.72) showed a low risk of developing ARI disease symptoms among children under the age of 5 years in Uganda. Conclusion Policy-makers and health stakeholders should initiate target-oriented approaches to address the problem regarding poor children’s healthcare, improper environmental conditions, and childcare facilities. For the sake of early child care, the government should promote child breastfeeding and maternal education.

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Section: Discussionsupporting

confidence: 81%

Analysis of risk factors associated with acute respiratory infections among under-five children in Uganda

Nshimiyimana

Zhou

2022

BMC Public Health

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“…A comparative study conducted for predicting the effective diagnosis of liver diseases using comparisons of methods such as Neural Network, Support Vector Machine, Random Forest, and Decision Tree, revealed that the Neural Network and Random Forest methods achieved the highest accuracy rate [31]. Another study carried out for predicting liver disorders showed that the Random Forest algorithm also performed well compared to the other methods employed in the study [32]. A study conducted by Marikani and Shyamala (2017) using clinical and demographic data revealed that RF (Accuracy = 96.3%) performed better in predicting heart disease [33], also similar to the Mani et al (2012) study conducted using Demographic and clinical test results in predicting type 2 diabetes [34].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Risk Factors Associated with Acute Respiratory Infections among Under-Five Children in Uganda

Nshimiyimana¹,

Zhou²

2021

Preprint

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Background: Globally, 7.4 million young children are being killed from infectious and treatable diseases, and Saharan Africa accounts for 90% of deaths. In Uganda, Acute Respiratory Infections (ARIs) remain the leading cause of childhood morbidity and mortality among under-five children. The study aims to identify and analyze contributing potential risk factors of childhood ARIs disease among under-five children in Uganda.Methods: A case-control study was conducted using data for 13,493 sampled under-five children retrieved from a recent 2016 Uganda Demographic and Health Survey. We compared two supervised tree-like structure machine learning algorithms and two logistic regression methods in terms of classification performances in predicting ARIs disease outcomes and analysing various child and parental socio-demographic, behavioural, and environmental characteristics.Results: The study results revealed that the ARIs prevalence among under-five children accounted 40.3% cases. The Logistic regression findings showed that the risk of developing childhood ARIs disease declined with increase in child's age where the risk of having ARIs was higher in children in one year of birth (AOR=1.27; p < 0:001) and lower in children aged four years old (AOR=0.69; p < 0:001) compared to the infants. Other factors such as the age of mother where children born from teen mothers (15-19 years) were high likely (AOR=1.28; p < 0:001) to have ARIs illness compared to those whose mothers were in the middle age groups, and children whose mothers breastfed showed a lower risk of ARIs disease (AOR=0.83; p < 0:001) compared to those who not breastfed. In the dry season, children were more likely to develop ARIs disease (AOR=1.34; p < 0:001) compared to the wet season, and factors such as the region of residences like central region, mother employment, and domestic cooking energy like wood were also potential risk factors of ARIs disease among under-five children in Uganda. In addition, three methods such as Decision Tree (Accuracy = 61.2%; AUC=0.610), Elastic Net Logistic Regression (Accuracy=61.7%; AUC=0.627), and Binary Logistic Regression (Accuracy=62.1%; AUC=0.638) showed approximately similar performances in predicting and classifying ARIs disease outcomes. However, the Random Forest (Accuracy=88.7%; AUC=0.951) showed superior difference in performance comparatively.Conclusion: Government and healthcare stakeholders need to make effective programs to improve public health policy against childhood infectious diseases by targeting the proper provision of maternal and child health-related education to household heads and mothers to adopt and prioritize breastfeeding practices, childcare, and ensure proper dwelling places for families and young children particularly in crowded regions and geographic places where ARIs prevalence is high.

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“…Thus, Multilayer Perceptron can be further used to diagnose the liver disorder efficiently. Another concept for diagnoses of liver disease was proposed by Pathan et al (2018). Various classification algorithms were used such as Naïve Bayes, Adaptive Boosting (Ada Boost), J48, Bagging and Random Forest.…”

Section: Literature Reviewmentioning

confidence: 99%

Liver Disease Detection

Mukhopadhyay

Samanta

Pan³

2023

International Journal of Healthcare Information Systems and Informatics

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In recent times, intelligent predictive systems are showing greater levels of accuracy and effectiveness in early detection of the critical diseases of cancer in the liver, lungs, etc. Predictive models assist medical practitioners to identify the diseases based on symptoms and health indicators like hormones, enzymes, age, blood counts, etc. This article focuses on proposing an optimal classification model to detect chronic liver disease by enhancing the prediction accuracy through cutting-edge analytics. The article proposes an enhanced framework on the original study by Ramana et al. It uses measures like precision and balanced accuracy to choose the most efficient classification algorithm in Indian and USA patient datasets using various factors like enzymes, age, etc. Using Youden's index, individual thresholds for each model were identified to increase the power of sensitivity and specificity, respectively. The study proposes a framework for highly accurate automated disease detection in the medical industry and helps in strategizing preventive measures for patients.

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Comparative Study of Different Classification Algorithms on ILPD Dataset to Predict Liver Disorder

Cited by 10 publications

References 6 publications

Analysis of risk factors associated with acute respiratory infections among under-five children in Uganda

Analysis of risk factors associated with acute respiratory infections among under-five children in Uganda

Analysis of Risk Factors Associated with Acute Respiratory Infections among Under-Five Children in Uganda

Liver Disease Detection

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