Smart home health monitoring system for predicting type 2 diabetes and hypertension

Chatrati, Saiteja Prasad; Hossain, Gahangir; Goyal, Ayush; Bhan, Anupama; Bhattacharya, Sayantan; Gaurav, Devottam; Tiwari, Sanju

doi:10.1016/j.jksuci.2020.01.010

Cited by 110 publications

(52 citation statements)

References 13 publications

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“…In such a technique, the new imputed value could be far from the central tendency of the population distribution. The performance in the pipeline (see Table 9) employed in [18], [20], [41], [42], [46] is less as comparing the proposed framework and others in [7], [44], [45]. Those fewer performances clearly indicate the role of outlier rejection and filling missing values in the PID dataset.…”

Section: E Results Comparisonmentioning

confidence: 99%

Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers

et al. 2020

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Diabetes, also known as chronic illness, is a group of metabolic diseases due to a high level of sugar in the blood over a long period. The risk factor and severity of diabetes can be reduced significantly if the precise early prediction is possible. The robust and accurate prediction of diabetes is highly challenging due to the limited number of labeled data and also the presence of outliers (or missing values) in the diabetes datasets. In this literature, we are proposing a robust framework for diabetes prediction where the outlier rejection, filling the missing values, data standardization, feature selection, K-fold cross-validation, and different Machine Learning (ML) classifiers (k-nearest Neighbour, Decision Trees, Random Forest, AdaBoost, Naive Bayes, and XGBoost) and Multilayer Perceptron (MLP) were employed. The weighted ensembling of different ML models is also proposed, in this literature, to improve the prediction of diabetes where the weights are estimated from the corresponding Area Under ROC Curve (AUC) of the ML model. AUC is chosen as the performance metric, which is then maximized during hyperparameter tuning using the grid search technique. All the experiments, in this literature, were conducted under the same experimental conditions using the Pima Indian Diabetes Dataset. From all the extensive experiments, our proposed ensembling classifier is the best performing classifier with the sensitivity, specificity, false omission rate, diagnostic odds ratio, and AUC as 0.789, 0.934, 0.092, 66.234, and 0.950 respectively which outperforms the state-of-the-art results by 2.00 % in AUC. Our proposed framework for the diabetes prediction outperforms the other methods discussed in the article. It can also provide better results on the same dataset which can lead to better performance in diabetes prediction. Our source code for diabetes prediction is made publicly available. INDEX TERMS Diabetes prediction, ensembling classifier, machine learning, multilayer perceptron, missing values and outliers, Pima Indian Diabetic dataset.

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Section: E Results Comparisonmentioning

confidence: 99%

Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers

et al. 2020

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“…Thirty-one studies conducted during the period 2010-2020 were included in the review. The topic of smart homes is very broad and can be looked at through many different perspectives: security [74], safety [75], health monitoring [76], social interaction [77], general well-being [43], support for carrying out activities of daily living, timely reminders for certain tasks or intake of medications. This review mainly focused on the aspects of health monitoring and environmental monitoring with use of technology involving the use of sensors, wearables, and robots.…”

Section: Discussionmentioning

confidence: 99%

Towards Privacy-Preserved Aging in Place: A Systematic Review

Gochoo

Alnajjar

Tan

et al. 2021

Sensors

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Owing to progressive population aging, elderly people (aged 65 and above) face challenges in carrying out activities of daily living, while placement of the elderly in a care facility is expensive and mentally taxing for them. Thus, there is a need to develop their own homes into smart homes using new technologies. However, this raises concerns of privacy and data security for users since it can be handled remotely. Hence, with advancing technologies it is important to overcome this challenge using privacy-preserving and non-intrusive models. For this review, 235 articles were scanned from databases, out of which 31 articles pertaining to in-home technologies that assist the elderly in living independently were shortlisted for inclusion. They described the adoption of various methodologies like different sensor-based mechanisms, wearables, camera-based techniques, robots, and machine learning strategies to provide a safe and comfortable environment to the elderly. Recent innovations have rendered these technologies more unobtrusive and privacy-preserving with increasing use of environmental sensors and less use of cameras and other devices that may compromise the privacy of individuals. There is a need to develop a comprehensive system for smart homes which ensures patient safety, privacy, and data security; in addition, robots should be integrated with the existing sensor-based platforms to assist in carrying out daily activities and therapies as required.

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“…FS can be considered as a multiobjective optimization problem in which two opposite objectives exist: the minimum number of selected features and higher classification accuracy. Therefore, to define the target function of FS, we need a classification algorithm, and since most of the authors [46][47][48][49][50] used the simplest classifier, that is, the KNN classifier, we also use this classifier in the proposed method to define the target function of the FS problem. KNN classifier is a widely used simple method for classification problems.…”

Section: Proposed Methodsmentioning

confidence: 99%

A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

Mohammadzadeh

Gharehchopogh

2020

Computational Intelligence

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Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition-based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar 176

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Smart home health monitoring system for predicting type 2 diabetes and hypertension

Cited by 110 publications

References 13 publications

Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers

Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers

Towards Privacy-Preserved Aging in Place: A Systematic Review

A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

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