Prediction of Severity of Diabetes Mellitus using Fuzzy Cognitive Maps

The objective to develop this research paper is concerned with a system which helps diagnose the severity of diabetes. The disease named diabetes mellitus makes the body unable to handle sugar so it causes thirst, frequency of urination, tiredness and many other symptoms. The diabetes mellitus describes a metabolic disorder characterized by chronic hyperglycemia with disturbances of carbohydrate, fat and protein metabolism resulting from defects in insulin secretion, insulin action, or both. It can be caused by number of factors like pancreatic dysfunction, obesity, hereditary, stress, drugs, alcohol etc. It includes long term damage, dysfunction and failure of various organs. The effects of diabetes mellitus include long term damage and failure of various organs. Diabetes mellitus may present with characteristic symptoms such as thirst, polyuria, blurring of vision, and weight loss. This Paper is implemented on soft computing technique, namely Fuzzy Cognitive Maps (FCM) to find out the presence or absence of diabetes mellitus based on the input of sign/symptoms recorded at three fuzzy levels developed by the domain experts. The large amount of data and information that needs to be handled and integrated requires specific methodologies and tools. The FCM based decision support system was developed with a view to help medical and nursing personnel to assess patient status assist in making a diagnosis. The software tool was tested on 50 cases, showing results with an accuracy of 96%. The analysis of experimental results of different applicants checks the correctness and consistency of decision Support system for correct decision making. Keywords: Fuzzy Logic, FCM, Diabetes Mellitus, Prediction, Symptoms

Deforestation and economic growth trends on oceanic islands highlight the need for meso-scale analysis and improved mid-range theory in conservation

Forests both support biodiversity and provide a wide range of benefits to people at multiple scales. Global and national remote sensing analyses of drivers of forest change generally focus on broad-scale influences on area (composition), ignoring arrangement (configuration). To explore meso-scale relationships, we compared forest composition and configuration to six indicators of economic growth over 23 years (1992-2015) of satellite data for 23 island nations. Based on global analyses, we expected to find clear relationships between economic growth and forest cover. Eleven islands lost 1 to 50% of forest cover, eight gained 1 to 28%, and four remained steady. Surprisingly, we found no clear relationship between economic growth trends and forest-cover change trajectories. These results differ from those of global land-cover change analyses and suggest that conservation-oriented policy and management approaches developed at both national and local scales are ignoring key meso-scale processes

The six sigma approach in performance management to reduce injury rate at work

This case study uses the Six Sigma process framework in performance management to explore and improve the injury rate of an international waste disposal firm. The results indicate that an employee-management consensus approach to continuous improvement in safety management in the workplace is essential. The evidence from this case suggested that the DMAIC Six Sigma process and analysis tool such as the fishbone diagram can be easily adopted as measurements in the workplace. Furthermore the case shows that management commitment and employee ownership of the Six Sigma program is the key to continuous improvement, and the development of a safety culture and a learning organisation

REGAL: Representation Learning-based Graph Alignment

Problems involving multiple networks are prevalent in many scientific and other domains. In particular, network alignment, or the task of identifying corresponding nodes in different networks, has applications across the social and natural sciences. Motivated by recent advancements in node representation learning for single-graph tasks, we propose REGAL (REpresentation learning-based Graph ALignment), a framework that leverages the power of automatically-learned node representations to match nodes across different graphs. Within REGAL we devise xNetMF, an elegant and principled node embedding formulation that uniquely generalizes to multi-network problems. Our results demonstrate the utility and promise of unsupervised representation learning-based network alignment in terms of both speed and accuracy. REGAL runs up to 30x faster in the representation learning stage than comparable methods, outperforms existing network alignment methods by 20 to 30% accuracy on average, and scales to networks with millions of nodes each.Comment: In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM), 201

Accuracy of smartphone based electrocardiogram for the detection of rhythm abnormalities in limb lead: a cross sectional study, non-randomised, single blinded and single-center study

Background: For the identification of arrhythmia and abnormal instances, researchers are examining the reliability of the interpretation offered by smartphone-based portable ECG monitors. The indicator of an unclear alteration in the electrical activity of the heart is a cardiac abnormality. As a result, its early and accurate identification can avoid myocardial infarction and even sudden cardiac death. Objectives of this study were to evaluate and validate the Spandan 12 lead ECG interpretation for accuracy in detection of the cardiac arrhythmias in comparison to the cardiologist diagnosis, and to evaluate the accuracy of the arrhythmia detection of Spandan ECG in comparison to the 12 lead ECG machine. Methods: This cross-sectional study, non-randomised, single blinded and single-center study was carried out at Shri Mahant Indresh Hospital (SMIH), Dehradun, Uttarakhand, India from 1st August 2022 to 31st January 2023. All patients (n=312) visiting the electrocardiogram (ECG) room at the department of cardiology of the SMIH, Dehradun with the prescription of ECG screening during the study period were included in the study were included in the study. Results: In total, 1528 patients with or without a history of cardiovascular disease were enrolled from outpatient and emergency departments of cardiology. A final total of 312 participants considered for accuracy of interpretation of cardiac arrhythmias detected by the standard 12 lead ECG and smartphone ECG in comparison to cardiologists’ diagnosis. Mean age (SD) was 53.90±14.52 years. The male gender (68.78%) showed the maximum frequency than female gender. True Positive cases derived from confusion matrix for 12 lead standard ECG and smartphone ECG in comparison to cardiologist diagnosis was 264 as compared to 273 from 12 lead gold standard. Sensitivity of smartphone Spandan ECG (81.23%) was comparable to gold standard 12 Lead ECG (81.49%). And, specificity, PPV and NPV of smartphone Spandan ECG was recorded to be better than gold standard 12 Lead ECG. Arrhythmia was detected correctly in 403 (70.8%) cases and 431 (61.86%) cases by smartphone ECG and 12 lead gold standards, respectively. Conclusions: Spandan ECG device scored a high accuracy and sensitivity and high specificity. The overall accuracy of smartphone ECG in detecting the rhythm abnormalities increase by 9%, the significance rises in accuracy of computer interpretation when compared to the cardiologist’s diagnosis

MRI Changes of the Spinal Subdural Space after Lumbar Spine Surgeries: Report of Two Cases

Although magnetic resonance imaging (MRI) is frequently used to assess the lumbar spine, there are few reports in the medical literature that have evaluated using MRI immediately following spinal surgery. Furthermore, descriptions of the subdural changes after lumbar spine surgery are also infrequent. In this paper, we present two cases with subdural change seen on MRI immediately after lumbar surgery. Both the patients had mild symptoms that resolved spontaneously, and the follow-up MRI scans showed resolution of the subdural changes. Subdural changes should be considered as one of the possible causes of unexpected symptoms in patients following lumbar spinal surgery

Modic type I changes of the lumbar spine in golfers

Low back pain (LBP) is the most prevalent musculoskeletal complaint among professional and amateur golfers; however, associated radiological changes in golf-related LBP have not been examined in the literature. We suspect that Modic Type 1 changes in the lumbar spine are linked to golf-related LBP. In this retrospective case series, four middle-aged golfers (one professional and three high-level amateurs) presented to our clinic with LBP. Inflammation of the right side of endplates in the lumbar spine was suspected based on Modic Type 1 changes detected by magnetic resonance imaging (MRI) in each patient. All four cases were diagnosed with right-sided endplate inflammation and administered intradiscal steroid injections with a non-steroidal anti-inflammatory drug (NSAID). Treatment swiftly alleviated LBP and diminished Modic Type 1 changes on follow-up MRI 3–6 months later in all four patients. We suggest that Modic Type 1 changes play a significant role in the diagnosis and treatment of golf-related LBP