Knowledge-driven machine learning based framework for early-stage disease risk prediction in edge environment

Hossain, M. Shamim; Ferdousi, Rahatara; Alhamid, Mohammed F.

doi:10.1016/j.jpdc.2020.07.003

Cited by 22 publications

(6 citation statements)

References 14 publications

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“…The observed trend indicates the widespread utilization of ML algorithms such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN), J48 Decision Tree (DT), C4.5 tree, and some incorporating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), among others. Notably, the RF algorithm consistently achieved high accuracy, reaching 97.40% [69], showcasing its effectiveness. Additionally, DT demonstrated commendable performance with an accuracy of 95.92% [23], particularly suited for smaller datasets.…”

Section: Machine Learning Algorithms For Dis-mentioning

confidence: 86%

A Systematic Review on IoT and Machine Learning Algorithms in E-Healthcare

Tenepalli,

T M

2024

IJCDS

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In recent years, the Internet of Things (IoT) has been adopted in many applications since its usage is essential to daily life. Also, it is a developing technology in the healthcare system to provide effective emergency services to patients. In the current scenario, medical cases and diseases among people are growing enormously. Thus, it is becoming challenging to accommodate and provide healthcare services for more incoming patients in clinics and hospitals with limited space and medical resources. Hence, the integration of IoT and assistive technologies came into the healthcare sector for providing efficient healthcare services wirelessly as well as for continuous monitoring of the patients. With the help of IoT and Machine Learning technologies, healthcare providers can keep a closer eye on their patients and maintain more proactive lines of communication with them. Data collected from IoT devices can be fed to Machine Learning technologies for predicting and diagnosing diseases. Due to the severity of diseases, lack of early disease prediction methods, lack of resources, and a smaller number of specialized doctors, most of the population is dying. Hence, to address these issues in the healthcare domain, more research works are proposed based on Machine Learning and IoT-based healthcare systems. This work reviews the research works related to IoT-based healthcare systems and machine learning comprehensively.

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Section: Machine Learning Algorithms For Dis-mentioning

confidence: 86%

A Systematic Review on IoT and Machine Learning Algorithms in E-Healthcare

Tenepalli,

T M

2024

IJCDS

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“…They obtained the highest success rate with RF in percentage split verification with 99% accuracy. Hossain et al (2020) proved that ignoring a specific symptom of the rule-based ontology plan in their proposed system could affect the performance of classifiers Özer (2020) achieved an average F1 score of 98.9% as a result of 10-fold cross validation with the Long Short Term Memory (LSTM) network. The accuracy value was 98.65%.…”

Section: Discussionmentioning

confidence: 99%

Possibility Prediction Of Diabetes Mellitus At Early Stage Via Stacked Ensemble Deep Neural Network

Yurttakal¹,

Baş²

2021

Afyon Kocatepe University Journal of Sciences and Engineering

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Diabetes Mellitus is a chronic metabolic disease caused by the deficiency of insülin action or secretion, or both, one of the hormones that balance the blood glocose level. It is one of the health problems that negatively affect people's quality of life. If diabetes is not detected in the early stages, it can cause serious complications such as heart and renal diseases, retinopathy, stroke, digestive disorders, and amputation. Because of the presence of a long asymptomatic period, early detection of diabetes is not realised usually. For this reason, around 50% of diabetic patients are not received a treatment due to undiagnosed at early stages. This situation results other diseases mentioned above, which diabetes causes. On the other hand, ensemble learning is a machine learning model in which multiple models are trained to solve the same problem and combined to achieve better results. Deep neural networks are one of the machine learning algorithms and they are the multi-layered state of artificial neural networks developed inspired by the information processing method of the human brain. In this study, a stacked ensemble-based deep neural network approach is proposed for diabetes possibility assessment in the early stages. The proposed approach was tested on a dataset of 520 patients. As a result, the proposed method achieved the highest success rate with 99.36% accuracy and 99.19% AUC, although the test percentage was kept higher than the prediction studies conducted on the same dataset.

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“…The core of generating training dataset in HCPS is to define an epidemiology library [19] for disease risk factors from real patient data. The patient data can be collected from a direct pre-screening questionnaire or via other means, which are approved and overseen by the healthcare practitioners, who also verify the class level of data.…”

Section: ) Training Dataset Generationmentioning

confidence: 99%

“…A knowledge base includes verified datasets, ontology, and rules to label data [19]. For example, risk ontology, symptom and disease ontology, medical rules to determine attribute value, and other information which can be repeatedly used to serve data query.…”

Section: ) Knowledge Basementioning

confidence: 99%

“…Epidemiological factors of NCDs generally stem from physical inactivity, alcoholic habit, diets, and other conditions. Hence, pre-screening and preventive measures are the keys to respond to NCDs [19]. The value of health transformation, the empowerment of wearable sensors, and ML must be broadly acknowledged in the fight against losses due to NCDs [20] [21] [22].…”

Section: Introductionmentioning

confidence: 99%

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Early-Stage Risk Prediction of Non-Communicable Disease Using Machine Learning in Health CPS

2021

Self Cite

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Cyber-Physical Systems (CPS) embed computation and communication capability into its core to regulate physical processes and seamlessly mediate between the cyber and the physical world for various control and monitoring tasks. Health CPS, a variant of CPS in the healthcare sector, acts as a health monitoring system to dynamically capture, process, and analyze health sensor data through integrated internet of things (IoT)-enabled cyber-physical processes. These systems can suitably support patients suffering from non-communicable diseases (NCDs) or who are at risk of suffering from those. Identifying the risk of NCDs, such as heart disease and diabetes, requires artificial intelligence (AI) techniques into the core of health CPS. Recently, there has been growing interest to incorporate machine learning into CPS, which can facilitate the disease classification, detection, monitoring, and prediction of several NCDs. However, there is a shortage of visible work that focus on early-stage risk prediction of these diseases. In this work, we propose a novel machine learning based health CPS framework that addresses the challenge of effectively processing the wearable IoT sensor data for early risk prediction of diabetes as an example of NCDs. In the experiment, a verified diabetic dataset has been used for training, while the testing has been performed on an artificially generated data collection from sensors. The experiment with several machine learning algorithms shows the effectiveness of the proposed approach in achieving the maximum precision from the Random Tree algorithm, which requires a minimum time of 0.01s to construct a model and obtains 94% accuracy to predict the probability of diabetes at an early point.

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Knowledge-driven machine learning based framework for early-stage disease risk prediction in edge environment

Cited by 22 publications

References 14 publications

A Systematic Review on IoT and Machine Learning Algorithms in E-Healthcare

A Systematic Review on IoT and Machine Learning Algorithms in E-Healthcare

Possibility Prediction Of Diabetes Mellitus At Early Stage Via Stacked Ensemble Deep Neural Network

Early-Stage Risk Prediction of Non-Communicable Disease Using Machine Learning in Health CPS

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