Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases (original) (raw)
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2021
Internet of Things (IoT) and cloud computing offers diverse applications in the medicinal sector by the integration of sensing and therapeutic gadgets. Medical expenses are rising gradually and different new diseases also exist globally, it becomes essential to transform the healthcare facilities from a hospital to patient-centric platform. For providing effective remote healthcare services to patients, this paper introduces an optimal IoT and cloud based decision support system for Chronic Kidney Disease (CKD) diagnosis. The proposed method makes use of simulated annealing (SA) based feature selection (FS) with Root Mean Square Propagation (RMSProp) Optimizer based Logistic Regression (LR) model called SA-RMSPO-LR to classify the existence of CKD from medical data. The proposed model involves a set of four subprocesses, which include data collection, preprocessing, FS, and classification. The inclusion of SA for FS helps to improvise the classifier results of the SA-RMSPO-LR model....
2021
Internet of Medical Things (IoMT) interlinks a collection of intelligent sensors on the patient's body to observe and interpret multimodal health data, including the patient's physiological and psychological signals. The large amount of data produced by IoMT devices in medical application is examined on cloud by replacing the restricted memory as well as processing resources of handheld tools. In this study, an IoMT-based healthcare diagnosis model is introduced by the use of intelligent techniques. This paper proposes a new IoMT-based disease diagnosis healthcare framework for heart disease prediction using the BBO-SVM model. The proposed model involves the parameter tuning of SVM using the BBO algorithm. The validation of the proposed model takes place using a Statlog Heart disease dataset. The detailed experimental analysis strongly pointed out that the proposed BBO-SVM model has shown excellent results by attaining a maximum precision of 88.33%, recall of 87.60%, accuracy of 89.26%, F-score of 87.96%, and kappa value of 78.27%.
Diagnosis of Chronic Kidney Disease Using Optimised Feature Selection and Ensemble Technique
2022
Chronic Kidney Disease (CKD) has been identified as an international challenge in healthcare that is increasing progressively. A survey showed that on average more than two million individuals over the world receive dialysis or transplanting kidney treatment to be alive. Prompt diagnosis of CKD is crucial. Prompt and applicable diagnosis demands the use of techniques in data mining. Recently, techniques now extend to a broad area in the diagnosis of a chronic kidney with importance mainly on accuracy via the simplification of disease by employing a selection of features together with pre-processing methods. This paper presented an optimised feature selection approach using the boosting of ensemble technique for CKD diagnostic model by the introduction of a nature-inspired computation algorithm known as Ant Colony Optimization for the selection of attributes from the CKD dataset. Seven selected learning algorithms were used for classification. The CKD diagnostic model was evaluated using an indigenous dataset collected from
2020
1, 2, 3Department of Computer Science and Engineering, Agni College of Technology 4Assistant professor, Computer Science and Engineering Department, Agni College of technology ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract Chronic Kidney Disease (CKD) need to be diagnosed earlier before kidneys fail to work.In order to help doctors or medical experts in prediction of CKD among patients easily, this paper has developed an intelligent system named Chronic Kidney Disease Prediction System (CKDPS) that can predict CKD among patients. The proposed system predict the CKD with minimal feature input instead of dumping all the features which may not relevant to predict the disease.To achieve this we have planned to approach by three feature selection algorithm with combination of two feature Extraction algorithm.After performing feature selection and Feature Extraction, those features will ...
International Journal of Engineering and Advanced Technology
Healthcare diagnosis system is very important and critical task in medical science for doctors and medical students. Chronic kidney disease is a very serious and dangerous problem which is directly related to the human life. In this research work, we have used data mining and feature selection technique to develop the robust and computationally efficient model for classifying chronic and non chronic kidney disease. An ensemble model is constructing through combination of two more similar types of trained model which helps to improve the performance. Feature selection is frequently used in machine learning area to raise a model with a few numbers of features which increase the performance of classification accuracy. The proposed feature selection techniques principle of Genetic Search (GS) and Greedy Stepwise Search (GSW). This proposed technique called GS-NB utilizes a pursuit methodology which is embedded in the Genetic Algorithm to select the features based on natural selection, t...
Bioengineering
The high prevalence of chronic kidney disease (CKD) is a significant public health concern globally. The condition has a high mortality rate, especially in developing countries. CKD often go undetected since there are no obvious early-stage symptoms. Meanwhile, early detection and on-time clinical intervention are necessary to reduce the disease progression. Machine learning (ML) models can provide an efficient and cost-effective computer-aided diagnosis to assist clinicians in achieving early CKD detection. This research proposed an approach to effectively detect CKD by combining the information-gain-based feature selection technique and a cost-sensitive adaptive boosting (AdaBoost) classifier. An approach like this could save CKD screening time and cost since only a few clinical test attributes would be needed for the diagnosis. The proposed approach was benchmarked against recently proposed CKD prediction methods and well-known classifiers. Among these classifiers, the proposed c...
IoT and Cloud based Smart Healthcare System using Optimized Data Classification Algorithm
Indian Journal of Science and Technology, 2019
Objectives: Presently, smart healthcare applications utilizing Internet of Things (IoT) offers vast number of features and real time services. They offer a real platform for billions of users to receive regular information related to health and better lifestyle. The usage of IoT components in the medicinal domain greatly helps to implement diverse characteristics of these applications. Methods: The huge volume of data created by the IoT devices in medicinal field is investigated on the cloud rather than mainly depends on available memory and processing resources of handheld devices. Keeping this idea in mind in this study, we try to devise an IoT and cloud based smart healthcare system to diagnose the disease. The IoT devices attached to the patient body gathers the needed data and stored in the cloud. Then, we present an optimal Support Vector Machine with Grey Wolf Optimization (SVM-GWO) algorithm to classify the presence of disease using the acquired data. For experimentation, we employ a benchmark heart disease dataset and a set of measures are used to analyze the attained results. Findings: The presented SVM-GWO achieves a maximum classifier results with accuracy of 84.07%, precision, recall and F-score of 84.10% respectively. Novelty: An optimal Support Vector Machine with Grey Wolf Optimization (SVM-GWO) algorithm is used to classify the presence of disease using the acquired data. The experimental outcome ensures the betterment of the presented model over the compared methods under different evaluation parameters.
IRJET, 2020
Chronic Kidney Disease (CKD) need to be diagnosed earlier before kidneys fail to work.In order to help doctors or medical experts in prediction of CKD among patients easily, this paper has developed an intelligent system named Chronic Kidney Disease Prediction System (CKDPS) that can predict CKD among patients. The proposed system predict the CKD with minimal feature input instead of dumping all the features which may not relevant to predict the disease.To achieve this we have planned to approach by three feature selection algorithm with combination of two feature Extraction algorithm.After performing feature selection and Feature Extraction, those features will be trained with different Machine Learning algorithm. The accuracy of best combination algorithm will be implemented for predicting the CKD.Finally, Random Forest algorithm is chosen to implement CKDPS as it gives 95% accuracy, precision and recall results.
Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm
IEEE, 2020
Chronic Kidney disease (CKD), a slow and late-diagnosed disease, is one of the most important problems of mortality rate in the medical sector nowadays. Based on this critical issue, a significant number of men and women are now suffering due to the lack of early screening systems and appropriate care each year. However, patients' lives can be saved with the fast detection of disease in the earliest stage. In addition, the evaluation process of machine learning algorithm can detect the stage of this deadly disease much quicker with a reliable dataset. In this paper, the overall study has been implemented based on four reliable approaches, such as Support Vector Machine (henceforth SVM), AdaBoost (henceforth AB), Linear Discriminant Analysis (henceforth LDA), and Gradient Boosting (henceforth GB) to get highly accurate results of prediction. These algorithms are implemented on an online dataset of UCI machine learning repository. The highest predictable accuracy is obtained from Gradient Boosting (GB) Classifiers which is about to 99.80% accuracy. Later, different performance evaluation metrics have also been displayed to show appropriate outcomes. To end with, the most efficient and optimized algorithms for the proposed job can be selected depending on these benchmarks.
Human-Centric Intelligent Systems
Chronic Kidney Disease (CKD) has become a major problem in modern times, and it is dubbed the silent assassin due to its delayed signs. To overcome these critical issues, early identification may minimize the prevalence of chronic diseases, though it is quite difficult because of different kinds of limitations in the dataset. The novelty of our study is that we extracted the best features from the dataset in order to provide the best classification models for diagnosing patients with chronic kidney disease. In our study, we used CKD patients’ clinical datasets to predict CKD using some popular machine learning algorithms. After handling missing values, K-means clustering has been performed. Then feature selection was done by applying the XGBoost feature selection algorithm. After selecting features from our dataset, we have used a variety of machine learning models to determine the best classification models, including Neural Network (NN), Random Forest (RF), Support Vector Machine ...