With the growth of technological devices, gadgets and utility products in routine life, there is need to escalate the energy optimization with higher degree of accuracy and performance. Earlier the 4G networks were used are quite prominent and the wireless scientists are working ahead towards the direction of 5G. In 5G based next-generation networks there are the projected features to transmit the huge amount of data and signals to the different locations whether to short or distant locations. The energy optimization, preservation and harvesting are key perspectives of research in advance gadgets in which the key focus is to minimize the energy loss and escalate the overall life period time of the network environment. These gadgets include assorted sensor nodes which communicate to each other using clustering and sharing of signals with the overall collaboration on the specific domain. In this manuscript, the mechanisms and methodologies for the energy parameter in the 5G networks are presented so that the greater accuracy and throughput can be obtained. In addition, a comparison has been established among the old classical network generations and the new 5G networks. The comparison is done predictably in terms of data rate, Latency, Mobility, Energy, and Efficiency of Spectrum. These specifications of the various network generations have been compared in order understand and highlight the benefits and advantages of the new coming generation (5G) over the features of the traditional network generations. Moreover, this paper is intended to show the challenges and related issues might be faced to achieve the implementation of the features and specifications of the new generation technology (5G). As a result, the new ( 5G ) will be more efficient and effective in terms of high data transfer rate, low latency, Mobility, and Energy. This is very important because it draws a road map for many exciting technologies and infrastructures including Internet of Things (IOT) , remote control of industrial machinery and robotics, and much faster download speed. Thus, 5G will support carrying huge amount of data faster which will help to support smarter and reliable technology infrastructures and environment. Keywords: 5G, wireless communications, New Radio, Mechanisms Prediction of energy, River Formation Dynamics (RFD), Nature Inspired Approach (NIA), Chanal State Information (CSI).

Background Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD. Methods We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics. Results Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio ≤ 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c ≤5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c ≤5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%. Conclusions Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help in early detection of patients with DM and CVD who could benefit from preventive treatment and reduce future healthcare burden.

Liver disease counts to be one of the most prevalent diseases in the worldwide. Therefore, this paper is aim to address the problem of predicting liver disease progression. As the existing predictive models focus on predicting the label of disease; the probability of developing the disease is still obscure. This paper, therefore, has proposed a model to predict the probability occurrence of liver diseases. The proposed predictive model used logistic regression abilities to predict the probability of liver disease occurrence. ILPD dataset was used to analyze the performance of the model. The predictive model has shown outstanding performance with a prediction accuracy rate of 72.4%, the sensitivity of 90.3%, the specificity of 78.3 %, Type I Error of 9.7 %, Type II Error of 21.7 %, and ROC of 0.758%. The model has furthermore confirmed the feasibility of the laboratory tests such as as (Age; Direct Bilirubin (DB), Alamine_Aminotransferase (SGPT), Total_Protiens (TP), Albumin (ALB)) to predict the disease progression. The predictive model will be helpful to patients and doctors to realize the progression of the disease and make a suitable timely intervention.

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Background: Cardiac autonomic neuropathy (CAN) is a diabetes-related complication with increasing prevalence and remains challenging to detect in clinical settings. Machine learning (ML) approaches have the potential to predict CAN using clinical data. In this study, we aimed to develop and evaluate the performance of an ML model to predict early CAN occurrence in patients with diabetes. Methods: We used the diabetes complications screening research initiative data set containing 200 CAN-related tests on more than 2000 participants with type 2 diabetes in Australia. Data were collected on peripheral nerve functions, Ewing’s tests, blood biochemistry, demographics, and medical history. The ML model was validated using 10-fold cross-validation, of which 90% were used in training the model and the remaining 10% was used in evaluating the performance of the model. Predictive accuracy was assessed by area under the receiver operating curve, and sensitivity, specificity, positive predictive value, and negative predictive value. Results: Of the 237 patients included, 105 were diagnosed with an early stage of CAN while the remaining 132 were healthy. The ML model showed outstanding performance for CAN prediction with receiver operating characteristic curve of 0.962 [95% confidence interval (CI) = 0.939–0.984], 87.34% accuracy, and 87.12% sensitivity. There was a significant and positive association between the ML model and CAN occurrence ( p < 0.001). Conclusion: Our ML model has the potential to detect CAN at an early stage using Ewing’s tests. This model might be useful for healthcare providers for predicting the occurrence of CAN in patients with diabetes, monitoring the progression, and providing timely intervention.

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