Abstract:
Recently, there has been a significant increase in the prevalence of physical illnesses,
including dengue disease, drawing considerable attention due to its impact on a large
population. The severity of the illness can be better understood by analyzing differences
between normal and affected diagnostic reports. With numerous studies focused on
understanding dengue disease, there are promising opportunities for advancing
diagnostic techniques. In this study, I used the utilization of algorithmic models for
early identification and raising awareness of potential threats. My straightforward
approach is suitable for predicting simple cases of dengue disease illness in real-world
scenarios. I have collected the dataset from Jamalpur Sadar Hospital. I employed
various classifiers, including Artificial Neural Network (ANN), Recurrent Neural
Network (RNN), Convolution Neural Network (CNN), Long Short-Term Memory
Network (LSTM), Bi-Directional Long Short-Term Memory Network (BLSTM),
Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), K-Nearest
Classifier (KNN), Adaboost Classifier (ABC), Decision Tree (DT), Support Vector
Machine (SVM), Quadratic Discriminant Analysis (QDA), Ridge Classifier (RC),
Passive Aggressive (PA), Gaussian Naïve Bayes (GNB) and ensemble techniques.
Notable results were achieved, with the Ridge Classifier (RC) standing out as the most
accurate, achieving an impressive accuracy rate of 96%. I implemented hyperparameter
tuning to optimize the performance of each classifier. Through an experimental
investigation and a review of recent findings, I confirmed that the bagging classifier
Ridge Classifier (RC) performed exceptionally well, accurately predicting dengue
disease with an accuracy rate of 96%.
