Covid-19 is a dangerous communicable virus which lets down the world economy. Severe respiratory syndrome SARS-COV-2 leads to Corona Virus Disease (COVID-19) and has the capability of transmission through human-to-human and surface-to-human transmission leads the world to catastrophic phase. Computational system based biological signal analysis helps medical officers in handling COVID-19 tasks like ECG monitoring at Intensive care, fatal ventricular fibrillation, etc., This paper is on diagnosing heart dysfunctions such as tachycardia, bradycardia, ventricular fibrillation, cardiac arrhythmia using fuzzy relations and artificial intelligence algorithm. In this study, the heart pulse base signal and features like spectral entropy, largest lyapunov exponent, Poincare plot and detrended fluctuation analysis are extracted and presented for classification purpose. The RR intervals of Poincare plot summarize RR time series obtained from an ECG in one picture, and a time interval quantities derives information duration of HRV. This analysis eases the prediction of heart rate fluctuation due to Covid or other heart disorders. The better accuracy level in diagnosing heart pulse irregularity using Artificial Neural network(ANN) is an integer value (0 to 4)but for Fuzzy Classifier, it is 0.8 to 0.9.The processing time for analyzing heart dysfunctionalties is 0.05s using ANN which is far better than Fuzzy classifier.
Background: Diabetes is a disease when left untreated, leads to many complications. India is emerging as a diabetic capital of the world. Insulin is widely used as a therapeutic option, and hence this study was conducted to assess the awareness of Insulin use and its adverse effects in diabetic population.Methods: The study was a questionnaire survey conducted in adult patients with diabetes who are on Insulin therapy. The participants’ knowledge, attitude and practice were assessed by using a questionnaire consisting of 32 questions. Scores were allotted to each question, and evaluated after applying appropriate statistical tests.Results: The mean age of the participants was 57.26±11.24 years. About 54% were males. 46% belonged to rural areas. The mean knowledge score was 17.53±4.40, mean attitude score 7.42±4.85 and the mean practice score was 6.56±1.91. 40% responded that they will return the expired insulin vials to the pharmacy. The most common reason for non adherence was economical constraints (60%). The females had better knowledge (17.60±4.43 vs. 17.45±4.40, p>0.88), attitude (8.21±3.84 vs. 6.58±5.56, p>0.09) and practice (6.97±1.84 vs. 6.13±1.92, p<0.02) of insulin use than males. Also, the urban population had better knowledge (17.58±3.64 vs. 17.32±4.97, p>0.297), attitude (8.70±3.95 vs. 6.06±5.37, p <0.002) and practice scores (6.92±1.89 vs. 6.38±1.92, p>1.395) than the rural counterparts.Conclusions: There exists a gap between knowledge attitude and practice of insulin use. This can be overcome by conducting awareness programmes by health care providers, to sensitise people about the proper use, side effects and the methods of disposal of insulin vials.
Data mining is an iterative development inside which development is characterized by exposure, through either usual or manual strategies. In this paper, we proposed a model to ensure the issues in existing framework in applying data mining procedures specifically Classification and Clustering which are connected to analyze the type of diabetes and its significance level for each patient from the data gathered. It includes the illnesses plasma glucose at any rate held value. The research describes algorithmic discussion of Support vect (SVM), Multilayer perceptron (MLP), Rule based classification algorithm (JRIP), J48 algorithm and Random Forest. The result SVM algorithm best. The best outcomes are accomplished by utilizing Weka tools.
The medical industry incredibly utilizes the data mining systems for different expectations and characterization. The substantial data repositories produced is subjected to different calculations to distinguish the examples in the data. The diabetic is the most undermining ailment with the end goal where millions of people suffers each year. In this paper the forecast of the diabetics is done by utilizing different procedures like classification and prediction techniques decision tree, Naive Bayes, Support vendor machine(SVM), clustering, K Neighbour, K-means, K-medoids, Neural Networks, Association rule mining and Multilayer Preceptron have been examined broadly. It is seen from the examination that the Naïve Bayes and C4.5 algorithm system show to have better execution with satisfactory results.
: Adverse drug reactions (ADR's) are the major cause of morbidity and mortality and most of the adverse drug reactions become evident only when the drug enters into the market, as clinical trials conducted on drugs involve only a limited number of subjects. Cutaneous manifestations of ADR's occur more frequently, hence this study was conducted to detect the morphological pattern, the common drugs causing cutaneous ADR's and to assess the severity of the same using Naranjo's algorithm. This was a prospective study conducted over a period of one year in the department of Dermatology and the department of Pharmacology. All the ADR's reported during the study period were confirmed by a dermatologist and assessed using Naranjo's algorithm. A total of 90 Cutaneous Adverse Drug Reactions (CADR's) were reported during the study period. Fixed drug eruption was the most common morphological pattern of ADR. Antibiotics were the most common drugs involved in causing CADR's. Most of the CADR's belonged to Probable category. Hence this study showed that CADR's are common to the drugs widely used, and the detection of the same will enable the treating physician to withdraw the use of the suspected drug. Also spontaneous reporting of ADR's will strengthen the Indian Pharmacovigilance database.
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