September 3, 2024 Thesis Open Access

Eyob Bitew

DCAT Export

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        <foaf:name>Eyob Bitew</foaf:name>
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    <dct:title>PREDICTING PIH LEVELS AND IDENTIFYING FACTORS USING MACHINE LEARNING</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2024</dct:issued>
    <dcat:keyword>Machine learning, supervised machine learning, PIH level prediction, XGboost, random forest, logistic regression, SVM, decision tree</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2024-09-03</dct:issued>
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    <dct:description>&lt;p&gt;Pregnancy-induced hypertension is a significant health concern that affects approximately 5-8% of pregnancies worldwide, contributing to maternal mortality. In Ethiopia, PIH is a leading cause of maternal mortality, emphasizing the need for early detection and intervention. This research investigates the application of machine learning algorithms to predict the levels of PIH in pregnant women, aiming to enhance the accuracy and timeliness of diagnosis. The importance of research integrating machine learning technologies in healthcare, particularly in resource-limited settings like Ethiopia, to improve maternal and fetal health outcomes. Using supervised machine learning techniques we have to perform by extracting variables like BMI,sBP,dBP, urine in protein, blood in sugar, and other variables are training using the given dataset. For those features we have to select an algorithm such as Random Forest, SVM, Decision tree, Logistic Regression, and XGboost model the study develops a predictive model capable of classifying PIH levels into normal, low, moderate, and high categories. The model&amp;#39;s performance is evaluated using metrics like accuracy, precision, recall, and F1-score. From this performance XGboost model has the highest Accuracy, Precision, Recall, and Fl-score are 98.56 %, 98.28 %, 98.93 %, and 98.58 % are measured respectively. Finally demonstrates its potential to assist healthcare providers in identifying factors and the level of hypertension from the predictive categories and implementing appropriate interventions. Future work can use ultrasound images can be used to detect structural abnormalities or blood flow issues that might correlate with PIH and Advanced image analysis techniques of algorithms, such as convolutional neural networks&lt;br&gt; &amp;nbsp;&lt;/p&gt;</dct:description>
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