Dengue detection using Machine Learning Algorithms

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Date

2022-07

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Publisher

International Islamic University Chittagong

Abstract

The dengue virus, a global health concern with an estimated 400 million annual infec tions, has posed a critical challenge worldwide. Between January 1 and August 7, 2023, Bangladesh experienced a concerning surge in dengue cases, reporting over 69,000 con firmed cases and 327 deaths according to the Ministry of Health and Family Welfare. Timely and accurate diagnosis is necessary for intervention and treatment optimization, possibly relieving severe complications and saving lives. In response, this paper intro duces a machine-learning approach to predict outbreaks in Chittagong and Cox’s Bazar. Through careful analysis of region-specific survey data, we identified local triggers for dengue spikes. Our data-driven model empowers healthcare officials with predictive ca pabilities to address future outbreaks, safeguarding communities. The study featuresa unique real-time dataset collected from healthcare institutions across Chittagong and Cox’s Bazar, involving collaboration with esteemed public and private institutions. By using diverse data, the dataset aims to unveil hidden insights into dengue outbreaks, guiding accurate predictions and effective prevention measures. The comprehensive pa tient dataset, including diagnoses, medical history, and symptoms, underwent precise model training with a 70:30 split. we have applied various machine learning algorithms, namely - SVM, Decision Tree, XGBoost, Naive Bayes, Random Forest, K-NN, Logistic Regression, and LDA. The Random Forest demonstrated accuracy of 98.92 percent on both training and test data. Performance assessment included a confusion matrixand macro averages for precision, recall, and F1-measure scores.

Description

This Thesis is Submitted in Fulfillment of the Requirements for the Degree of Bachelor of Science (B.Sc.) in Computer Science and Engineering (CSE) Spring-2022

Keywords

SVM, Decision Tree, XGBoost, Naive Bayes, Random Forest, K-NN, Lo- gistic Regression, LDA

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