IoT based Schistosomiasis Monitoring for More Efficient Disease Prediction and Control Model

Kasse, Bassirou; Gueye, Bamba; Diallo, Moussa; Santatra, Fiorenantsoa; Elbiaze, Halima

doi:10.1109/sas.2019.8706019

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…In [24], a multipurpose IoT-based monitoring model was proposed to analyze the sensor data to predict the arthritis infections by IoT smart devices. Also, recently, in [25], an IoT-based schistosomiasis monitoring framework was proposed for more efficient disease prediction. Moreover, an IoT-based heart failure prediction and analysis model through machine learning methods was proposed in [26], and also, recently, in [27], a deep learning framework for prediction of heart disease was proposed for IoT context.…”

Section: Remote Health Monitoring Framework and Architectures In Iotmentioning

confidence: 99%

RETRACTED ARTICLE: A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

Akhbarifar

Javadi

Rahmani

et al. 2020

Pers Ubiquit Comput

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Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients’ health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients’ health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients’ health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients’ sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.

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Section: Remote Health Monitoring Framework and Architectures In Iotmentioning

confidence: 99%

RETRACTED ARTICLE: A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

Akhbarifar

Javadi

Rahmani

et al. 2020

Pers Ubiquit Comput

View full text Add to dashboard Cite

show abstract

“…Effective results were obtained by using SVM for training and testing images. Data was gathered through a network of wireless sensors, and Kasse et al [ 20 ] developed a system based on IoT monitoring that can help control and predict disease. For disease detection and transmission, multiple data mining algorithms were applied.…”

Section: Literature Surveymentioning

confidence: 99%

A Proposed Framework for Early Prediction of Schistosomiasis

et al. 2022

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Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation. Early prediction and identification of risk factors may aid in treating disease before it becomes incurable. We aimed to create a framework by incorporating the most significant features to predict Schistosomiasis using machine learning techniques. A dataset of advanced Schistosomiasis has been employed containing recovery and death cases. A total data of 4316 individuals containing recovery and death cases were included in this research. The dataset contains demographics, socioeconomic, and clinical factors with lab reports. Data preprocessing techniques (missing values imputation, outlier removal, data normalisation, and data transformation) have also been employed for better results. Feature selection techniques, including correlation-based feature selection, Information gain, gain ratio, ReliefF, and OneR, have been utilised to minimise a large number of features. Data resampling algorithms, including Random undersampling, Random oversampling, Cluster Centroid, Near miss, and SMOTE, are applied to address the data imbalance problem. We applied four machine learning algorithms to construct the model: Gradient Boosting, Light Gradient Boosting, Extreme Gradient Boosting and CatBoost. The performance of the proposed framework has been evaluated based on Accuracy, Precision, Recall and F1-Score. The results of our proposed framework stated that the CatBoost model showed the best performance with the highest accuracy of (87.1%) compared with Gradient Boosting (86%), Light Gradient Boosting (86.7%) and Extreme Gradient Boosting (86.9%). Our proposed framework will assist doctors and healthcare professionals in the early diagnosis of Schistosomiasis.

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“…Confusion matrix indicated the best prediction of disease is performed by ANN model than others. Kassé et al [21] developed an IoT based monitoring system for prediction and controlling of disease and collected data through wireless based sensors network. They applied different data mining algorithms for disease detection and transmission which shows the SVM shows detection of anomalies better than other models.…”

Section: Related Workmentioning

confidence: 99%

Detection of Schistosomiasis Factors Using Association Rule Mining

et al. 2019

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Bilharzia or schistosomiasis is one of the most fatal and factitious disease happens through pollute which become a significant reason of deaths in the world. Prediction and factors identification that become causes of disease in early stage, may escort to treatment before it becomes critical. Data mining techniques are used to assist medical professionals effectively in diseases' classification. This research investigates the recovery and death factors which contributes to schistosomiasis disease preprocessed dataset, collected from Hubei, China. A computerized learning method, association rule mining (Apriori) is used to spot factors. Different tools were used for analysis and model evaluation with minimum support and minimum confidence indicated higher than 90% to generate rules. In addition, attributes indicating recovery and death of individuals were identified. Strong associations of disease factors; BMI, viability, nourishment, extent to ascites etc. determined and classified through Apriori algorithm. Further, results generated by association rule mining method may useful for professionals in treatment decision with better precision.

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IoT based Schistosomiasis Monitoring for More Efficient Disease Prediction and Control Model

Cited by 7 publications

References 11 publications

RETRACTED ARTICLE: A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

RETRACTED ARTICLE: A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

A Proposed Framework for Early Prediction of Schistosomiasis

Detection of Schistosomiasis Factors Using Association Rule Mining

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