Remote health monitoring applications with the advent of Internet of Things (IoT) technologies have changed traditional healthcare services. Additionally, in terms of personalized healthcare and disease prevention services, these depend primarily on the strategy used to derive knowledge from the analysis of lifestyle factors and activities. Through the use of intelligent data retrieval and classification models, it is possible to study disease, or even predict any abnormal health conditions. To predict such abnormality, the Convolutional neural network (CNN) model is used, which can detect the knowledge related to disease prediction accurately from unstructured medical health records. However, CNN uses a large amount of memory if it uses a fully connected network structure. Moreover, the increase in the number of layers can lead to an increase in the complexity analysis of the model. Therefore, to overcome these limitations of the CNN-model, we propose a CNN-regular target detection and recognition model based on the Pearson Correlation Coefficient and regular pattern behavior, where the term ''regular'' denotes objects that generally appear in similar contexts and have structures with low variability. In this framework, we develop a CNN-regular pattern discovery model for data classification. First, the most important health-related factors are selected in the first hidden layer, then in the second layer, a correlation coefficient analysis is conducted to classify the positively and negatively correlated health factors. Moreover, regular patterns' behaviors are discovered through mining the regular pattern occurrence among the classified health factors. The output of the model is subdivided into regular-correlated parameters related to obesity, high blood pressure, and diabetes. Two distinct datasets are adopted to mitigate the effects of the CNN-regular knowledge discovery model. The experimental results show that the proposed model has better accuracy, and low computational load, compared with three different machine learning techniques methods.
Obesity is considered one of the leading causes of chronic and noncommunicable diseases; these include diabetes, cardiovascular disease, and cancer. The obesity prevalence is threefold higher in the Arab Gulf Cooperation Council (GCC) population than the rest of the world and leaves healthcare providers within the region with no alternative than to offer continuous and sustainable healthcare services. Obesity prevention would be more economical for governments than providing treatment. Preventing obesity is challenging because it requires motivating individuals to live a healthy lifestyle. Personal health (pHealth) has recently been actively involved in finding solutions to encourage healthy living. However, pHealth does not address the high percentage of people lacking the desire to maintain healthy living plans, which could have a negative effect on attempts aimed at reducing obesity prevalence. This study sheds light on the challenges faced by the GCC governments in reducing high obesity rates using pHealth; we propose a solution, Wholesome Coin, which incorporates advanced technologies to help governments reduce high obesity rates. Wholesome Coin has two components: one uses wearable IoT (WIoT) to help patients manage their behavior by tracking their physical activities and diet, and the other utilizes blockchain technology to help healthcare payers to incentify patients to maintain a healthy living plan by awarding digital coins that can be redeemed for real goods and services. GCC governments’ adoption of Wholesome Coin could improve the quality of life of obese patients in a seamless, secure, and self-motivated manner, resulting in a healthier tomorrow, especially amid challenging times featuring global social distance campaigns.
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