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![Figure 3: Ensemble model architecture [10].](profile/Naiyar-Iqbal/publication/337193077/figure/fig2/AS:824409736634370@1573566006720/Ensemble-model-architecture-10_Q320.jpg)
Dengue disease patients are increasing rapidly and actually dengue has recorded in every continent today
according to the World Health Organization (WHO) record. By WHO report the number of dengue
outbreak cases announced every year has expanded from 0.4 to 1.3 million during the period of 1996 to
2005 and then it has reached to 2.2 to 3.2 milli...
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Context 1
... model is a new way to the mixture of numerous prominent models for enhancement of the precision rate of a novel model for better prediction. It is a combination of k-learned models (M1, M2, M3...Mk) with the purpose of making an upgraded model M* [10], shown in figure 3. ...
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Fever is the most normal disease in any age group, but it becomes a deadly disease if it has dengue symptoms. Identifying dengue symptoms at an early stage is very difficult because this kind of symptoms is very common in all types of fevers. When fever continues after 3-4 days the symptoms of dengue shown in patients. So far there is no vaccine (A...
Recently machine learning algorithms are widely used for the prediction of different attributes, and these algorithms find widespread applications in a variety of domains. Machine learning in health care has been one of the core areas of research where machine learning models are used on the medical datasets to predict different attributes. This wo...
Automated machine learning (AutoML) systems aim to enable training machine learning (ML) models for non-ML experts. A shortcoming of these systems is that when they fail to produce a model with high accuracy, the user has no path to improve the model other than hiring a data scientist or learning ML -- this defeats the purpose of AutoML and limits...
In this study, we focus on the classification of Sinhala social media sentiments into positive and negative classes for a particular domain (sports). We have employed machine learning algorithms and lexicon-based sentiment classification methods. We also consider a hybrid approach by constructing an ensemble classifier in which we combine Machine L...
Network attacks are increasing day by day. In order to detect them, a system has been created, which actively detects intrusions and attacks in a network or an intranet. The system that detects these types of attacks and intrusions is called intrusion detection system (IDS). The attacks are of two kinds, known and unknown. The IDSs are able to prot...
Citations
... Iqbal et al. [48] concentrated on building a model that can effectively recognise the extent of the dengue outbreak in a large number of specimens at once. Seven well-known machine learning systems were evaluated for their capacity to predict dengue outbreaks. ...
... Triage systems aim, not only to ensure clinical justice for the patient, but also to provide an effective tool for departmental organization, monitoring and evaluation. It is most commonly used to describe the prioritization of patients based on their need for treatment (Iqbal & Islam, 2019). Triage allows first responders, who may lack resources, to prioritize care. ...
COVID-19 had been the most threatening virus in the world today. Over 200 countries had been affected with a record of 37.7 million people as at October 2020. In Nigeria, the number of recorded confirmed cases by Nigeria Centre for Disease Control (NCDC) has been above 58,000 people. The disease can cause a number of great discomforts to an individual and might sometimes lead to death if immediate attention is not given to the patient. The daily increase in COVID-19 requires more sufficient resources which are said to be inadequate, such as nursing and physician shortage, space constraints and equipment. The management of the diseases requires social and cultural sensitivity of expert and experience of the practitioner due to the challenging condition of the patient. The condition of COVID-19 patient needs immediate attention in triaging in order to quickly identify the present state and subsequently make refer to the right department. Neural Network (NN) is one of the types of Artificial Intelligence (AI) technique that can be used to triage COVID-19 patient in the emergency department (ED) operations. Emergency Severity Index (ESI) of 5-level triage algorithm was used to categorize the crisis based on the severity. This paper considered Neural Network framework in triaging COVID-19 based on the symptoms exhibited by the patient. The solution here provides a standard, comprehensive and effective way of better management of healthcare resources and elimination of complexity in identifying and treating the patient thereby improving the quality of life
... In one widely-explored use case, ML has proven itself effective in predicting outbreaks of dengue [29][30][31][32][33][34] . Anyamba et. ...
The pork industry is an essential part of the global food system, providing a significant source of protein for people around the world. A major factor restraining productivity in the pork industry is disease outbreaks in pigs throughout the production process: widespread outbreaks can lead to losses as high as 10% of the U.S. pig population in extreme years. In this study, we present a model to predict the emergence of outbreaks of swine farms throughout the production process. We capture direct contact, spatio-temporal and historical predictors, each represented through a set of features, and then train and evaluate machine learning algorithms on our extracted feature sets. We perform a feature selection to determine the smallest subset of features that provides good performance and use the results to interpret the most valuable features and produce the most generalizable model to address issues caused by the curse of dimensionality. Finally, we evaluate the model's ability to predict outbreaks in both the near and distant future, which allows for advance warning of disease outbreak. We evaluate our model on two swine production systems; our results demonstrate good ability to predict outbreaks in both systems with a balanced accuracy of 0.798 on any disease in the first system and balanced accuracies of 0.638, 0.709, and 0.701 on porcine reproductive and respiratory syndrome, influenza A virus, and Mycoplasma hyopneumoniae in the second system, respectively.
... During training, LogitBoost uses diverse data samples and generates an output prediction. As an ensemble, LogitBoost follows the boosting approach [23]. The boosting approach is the most powerful learning method, and it is used for both classification and regression analysis [23,24]. ...
... As an ensemble, LogitBoost follows the boosting approach [23]. The boosting approach is the most powerful learning method, and it is used for both classification and regression analysis [23,24]. This weak model is boosted to increase performance and give high output forecasts. ...
... The ANN method consists of processing units called neurons where these neurons have a function that can determine the activation of the neuron, the function is called activation which processes input signals that have been combined, then converts them into output signals [41]. To calculate the sum of the product weights xiwkj (for i=0 to m) is usually denoted as netk as shown in equation 6 [42]. ...
... artificial neuron calculates the output yk as a certain function of the netk value defined in equation 7 [42]. ...
... Where x and y are input and output signals, wkj synaptic weights, synapses, and f is the activation function [42]. ...
This study analyzes the performance of hybrid methods in improving accuracy on imbalanced data using Dengue Hemorrhagic Fever Case Data from 2017 to 2021 in Bandung City. The attributes used in this study consist of Total Population, Total Male, Elementary School Graduation, Junior High School Graduation, High School Graduation, College Graduation, Rainfall, Average Temperature, Humidity, Male Cases, Number of Cases, and Class. This research combines five Machine Learning methods, such as Decision Tree, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor, and Naïve Bayes. Hybrid Methods used in this research are Voting and Stacking methods. The oversampling methods used to handle imbalanced data in this study are Random Oversampling and Adasyn. The results show that Voting and Stacking without Random Oversampling and Adasyn get the same accuracy of 88,88%. While using Random Oversampling, voting gets an accuracy of 95,37% and stacking gets an accuracy of 96,29%. While using Adasyn, voting gets an accuracy of 94,44% and stacking gets an accuracy of 97,22%. Based on the results obtained, it can be concluded that the Random Oversampling and Adasyn Method can improve the performance of the Machine Learning hybrid method on imbalanced data. The contribution of this research is to provide information on the study and analysis of the implementation of the Random Oversampling and Adasyn methods in improving the performance of the Voting and Stacking methods in hybrid classification.
... We conducted further analysis of the soft-computing techniques in terms of the frequency of algorithms used, the goal of the study-prediction, classification, analysis or evaluation, and usage trend over the period under consideration. (categorized into one group); this category focused on the prognosis and prediction of diseases such as malaria, typhoid, dengue fever, and other tropical diseases [55][56][57]. The second category (27.2%) grouped articles with algorithm goals like identification and classification of dengue fever and other tropical diseases [58][59][60]. ...
This systematic literature aims to identify soft computing techniques currently utilized in diagnosing tropical febrile diseases and explore the data characteristics and features used for diagnoses, algorithm accuracy, and the limitations of current studies. The goal of this study is therefore centralized around determining the extent to which soft computing techniques have positively impacted the quality of physician care and their effectiveness in tropical disease diagnosis. The study has used PRISMA guidelines to identify paper selection and inclusion/exclusion criteria. It was determined that the highest frequency of articles utilized ensemble techniques for classification, prediction, analysis, diagnosis, etc., over single machine learning techniques, followed by neural networks. The results identified dengue fever as the most studied disease, followed by malaria and tuberculosis. It was also revealed that accuracy was the most common metric utilized to evaluate the predictive capability of a classification mode. The information presented within these studies benefits frontline healthcare workers who could depend on soft computing techniques for accurate diagnoses of tropical diseases. Although our research shows an increasing interest in using machine learning techniques for diagnosing tropical diseases, there still needs to be more studies. Hence, recommendations and directions for future research are proposed.
... Similarly, in another study, the system generated warnings by SMS and emails once they received a confirmed diagnosis and treatment from the doctor [47]. Although the studies on IoT sensor-based healthcare claimed the proposed system to be more efficient and effective than the existing ones, the inclusion of the traditional security aspect in the proposed models becomes a major setback against hackers, and a new cryptography mechanism has been suggested to enhance the security of IoT-based healthcare systems [48,49]. ...
Dengue fever has earned the title of a rapidly growing global epidemic since the disease-causing mosquito has adapted to colder countries, breaking the notion of dengue being a tropical/subtropical disease only. This infectious time bomb demands timely and proper treatment as it affects vital body functions, often resulting in multiple organ failures once thrombocytopenia and internal bleeding manifest in the patients, adding to morbidity and mortality. In this paper, a tool is used for data collection and analysis for predicting dengue infection presence and estimating risk levels to identify which group of dengue infections the patient suffers from, using a machine-learning-based tertiary classification technique. Based on symptomatic and clinical investigations, the system performs real-time diagnosis. It uses warning indicators to alert the patient of possible internal hemorrhage, warning them to seek medical assistance in case of this disease-related emergency. The proposed model predicts infection levels in a patient based on the classification provided by the World Health Organization, i.e., dengue fever, dengue hemorrhagic fever, and dengue shock syndrome, acquiring considerably high accuracy of over 90% along with high sensitivity and specificity values. The experimental evaluation of the proposed model acknowledges performance efficiency and utilization through statistical approaches.
... Naiyar Iqbal and Mohammad Islam [13] used 7 distinct machine learning techniques to predict dengue outbreaks. The dataset they gathered from patients' individual test results consisted of 75 patients, among them, 36 of them were dengue negative, and the rest of them were dengue infected. ...
Preoperative events can be predicted using deep learning-based forecasting techniques. It can help to improve future decision-making. Deep learning has traditionally been used to identify and evaluate adverse risks in a variety of major applications. Numerous prediction approaches are commonly applied to deal with forecasting challenges. The number of infected people, as well as the mortality rate of COVID-19, is increasing every day. Many countries, including India, Brazil, and the United States, were severely affected; however, since the very first case was identified, the transmission rate has decreased dramatically after a set time period. Bangladesh, on the other hand, was unable to keep the rate of infection low. In this situation, several methods have been developed to forecast the number of affected, time to recover, and the number of deaths. This research illustrates the ability of DL models to forecast the number of affected and dead people as a result of COVID-19, which is now regarded as a possible threat to humanity. As part of this study, we developed an LSTM based method to predict the next 100 days of death and newly identified COVID-19 cases in Bangladesh. To do this experiment we collect data on death and newly detected COVID-19 cases through Bangladesh’s national COVID-19 help desk website. After collecting data we processed it to make a dataset for training our LSTM model. After completing the training, we predict our model with the test dataset. The result of our model is very robust on the basis of the training and testing dataset. Finally, we forecast the subsequent 100 days of deaths and newly infected COVID-19 cases in Bangladesh.
... Naiyar Iqbal and Mohammad Islam [13] used 7 distinct machine learning techniques to predict dengue outbreaks. The dataset they gathered from patients' individual test results consisted of 75 patients, among them, 36 of them were dengue negative, and the rest of them were dengue infected. ...
Preoperative events can be predicted using deep learning-based forecasting techniques. It can help to improve future decision-making. Deep learning has traditionally been used to identify and evaluate adverse risks in a variety of major applications. Numerous prediction approaches are commonly applied to deal with forecasting challenges. The number of infected people, as well as the mortality rate of COVID-19, is increasing every day. Many countries, including India, Brazil, and the United States, were severely affected; however, since the very first case was identified, the transmission rate has decreased dramatically after a set time period. Bangladesh, on the other hand, was unable to keep the rate of infection low. In this situation, several methods have been developed to forecast the number of affected, time to recover, and the number of deaths. This research illustrates the ability of DL models to forecast the number of affected and dead people as a result of COVID-19, which is now regarded as a possible threat to humanity. As part of this study, we developed an LSTM based method to predict the next 100 days of death and newly identified COVID-19 cases in Bangladesh. To do this experiment we collect data on death and newly detected COVID-19 cases through Bangladesh's national COVID help desk website. After collecting data we processed it to make a dataset for training our LSTM model. After completing the training, we predict our model with the test dataset. The result of our model is very robust on the basis of the training and testing dataset. Finally, we forecast the subsequent 100 days of deaths and newly infected COVID-19 cases in Bangladesh.
... In paper [31], the author has used machine to classify dengue infected patients as well as no In paper [33], the researcher has conducted a performance evaluation test of seven Machine ...
... In this paper [43], the author has developed a prediction model to diagnose dengue outbreak on the basic climate data. The finding of this research is number of cases are identified in three periods namely 52 epidemiological weeks, pre-epidemic (week 10-20) , epidemic (week [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. The prediction model has achieved 70% percent accuracy. ...
Vector Borne diseases are rising challenge in India. These have become a burden for the society and prevention and control of these vector borne diseases is still a challenge for the government. Large portion of population is India is infected from this disease every year. Due to the diversity in geographical and living standard of people, it becomes difficult to control these diseases at early stages in the present system. The goal of this study is to investigate the symptoms and study the influence of clinical test parameters that belong to vector borne disease. The main aim of this study is to develop a prediction model using machine learning techniques for vector borne diseases. Vector Borne diseases is a vast area pertaining to research in which a large number of diseases are covered. But the focus of this research is limited to dengue disease as it has been the one of the most prevailing during the recent years. The study aims to propose a prediction model which is capable of diagnosing Dengue at early stages with an Indian perspective and also classifying the stages of clinically confirmed Dengue cases. The proposed model consists of five modules namely: Data Transformation, Data Pre-processing, Feature scaling & Normalization, Split dataset, Model Building and Prediction module. In the first module, data transformation process has been done. In the second module, process of data pre-processing, feature scaling and Normalization of dataset has been performed. In the third module, data pre-processing and normalization process has been performed. In the fourth module, the prediction model has been prepared with the help of Gaussian Naïve Byes classification. At the end, study proposes a prediction model for vector borne disease capable of diagnosing dengue disease at each stage of Dengue. The novelty of the proposed prediction model is that it is capable to predict dengue at early stages as well as classifying the type of dengue with the help of clinical report of patient. The proposed model has been tested and validated using five machine learning algorithms namely Decision Tree, Logistic Regression, Support Vector Machine (SVM), Random Forest Classifier and Gaussian Naive Bayes Classifier. The proposed model achieved 97.5% average accuracy after testing and validation process. But Gaussian Naïve Bayes classifier has provided 97.5% accuracy and 0 % Means Square Error. This study can further be extended to other vector borne diseases namely chikungunya, Zika, Kala Azar etc. with help of machine learning algorithms.
