Classification Model for Meticulous Presaging of Heart Disease Detection through SDA and NCA using Machine learning :CMSDANCA

For the design and implementation of CDSS, computation time and prognostic accuracy are very important. To analyze the large collection of a dataset for detecting and diagnosis disease ML techniques are used. According to the reports of World Health Organizations, HD is a major cause of death and killer in urban and rural areas or worldwide. The main reason for this is a shortage of doctors and delay in the diagnosis. In this research work, heart disease is a diagnosis by the data mining techniques and used the clinical parameters of patients for early stages diagnosis. The intend of this learning to develop a representation that relies on the prediction method for coronary heart disease. This proposed work used the approach of self-diagnosis Algorithm, Fuzzy Artificial neural network, and NCA & PCA and imputation methods. By the use of this technique computation time for prediction of Coronary HD can be reduced. For the implementation of this the two datasets are using such as Cleveland and Statlog datasets that is collected from the UCI kaggle the ML repository. The datasets for the disease prediction measure are used to accurately calculate the difference between variables and to determine whether they are correlated or not. For this classification model, the performance measure is calculated in requisites of their accuracy, precision, recall, and specificity. This approach is evaluated on the heart disease datasets for improving the accuracy performance results obtained. The outcome for KNN+SDA+NCA+FuzzyANN for Cleveland dataset accuracy achieved 98.56 %.and for Statlog dataset 98.66 %.

On a generalization of Hankel operators via operator equations

In this paper, the notion of (λ, μ)-Hankel operators on the space H² is introduced. Along with discussion of some of its properties, the paper also presents a result for (λ, μ)- Hankel operators which is similar to the classical theorem of Kronecker known for Hankel operators.peerReviewe

A Hybrid Detection Model for Meticulous Presaging of Heart Disease using Deep Learning: HDMPHD

Heart diseases that occur due to the blockage of coronary arteries, which causes heart attack, are also commonly known as myocardial infarction. Rapid detection and acute diagnosis of myocardial infarction avoid death. The electrocardiographic test or ECG signals are used to diagnosis myocardial infarction with the help of ST variations in the heart rhythm. ECG helps to detect whether the patient is normal and suffering from myocardial infarction. In blood, when the enzyme value increases, after a certain time pass occurs, heart attack. For ECG images, the manual reviewing process is a very difficult task. Due to advancements in technology, computer-aided tools and software are used to diagnosis myocardial infarction,because manual ECG requires more expertise .so that automatic detection of myocardial infarction on ECG could be done by different machine learning tools. This study detects the normal and myocardial infarction patients by selecting the feature with their feature weights by selecting from the model and by Random forest classifier selecting the index value using DenseNet-121, ResNet_50, and EfficientNet_b0 deep learning techniques .This proposed work used the real dataset from Medanta hospital (India) at the time of covid 19. The dataset is in the form of ECG images for Normal and myocardial infarction (960 samples). With an end-to-end structure, deep learning implements the standard 12-lead ECG signals for the detection of normal and myocardial infarction..The proposed model provides high performance on normal and myocardial infarction detection. The accuracy achieved by the proposed model for Efficientnet_b0 Random Forest to Select from Model Accuracy 84.244792, Precision 84.396532, Recall 84.227410, F-Measure 84.222295

Yield of different bronchoscopic techniques in diagnosis of lung cancer

Background: Lung cancer is generally diagnosed during late stage of the disease so early diagnosis of lung cancer is very important to reduce lung cancer death rate. Flexible fibreoptic bronchoscopy revolutionized early diagnosis of lung cancer as it provides sufficient cytologic and histologic specimens in form of bronchial brushings, broncho-alveolar lavage and bronchial forceps biopsy. Cytologic techniques are safe, economical, and provide quick results. They not only complement tissue biopsies in the diagnosis of lung cancer but are also comparable in yield.Methods: The present study analyzes cytology of Bronchoalveolar lavage, Bronchial Brushings and histology of bronchial biopsy in 45 patients diagnosed as lung cancer by fiber-optic bronchoscopy. Age, gender, smoking habits, various histological types of malignancies, and yield of various bronchoscopic diagnostic techniques in the diagnosis of lung cancer were evaluated.Results: Of the 45 cases with confirmed diagnosis, 37 (82.22%) were males and 8 (17.17%) were females with male to female ratio of 4.6:1. The mean age in this study group was 54.71 years. Squamous cell carcinoma was the most common primary bronchogenic tumor (62.22%) followed by adeno carcinoma (26.66%). The overall diagnostic yield of fiber-optic bronchoscopy procedures was 100% (32/32 patients) in bronchoscopically visible tumors. Bronchial biopsy was most sensitive (100%) followed by Bronchial Brushings (88%) and BAL (81.25%). However, in non-visible tumors, biopsy, brush and BAL yielded diagnostic specimens for lung cancer in 84.61%, 76.92% and 46.15% of patients respectively.Conclusions: Lung cancer is a common malignancy with male preponderance. Bronchial biopsy has a very high diagnostic yield. Cytopathological examination of bronchial brushings and broncho-alveolar lavage not only complement tissue biopsies in the diagnosis of lung cancer but have comparable diagnostic yield

Dosimetric study of hypo fractionated adjuvant post mastectomy radiotherapy with and without bolus and assessment of acute toxicity of treatment: a single institution study

Background: Moderate hypo fractionated PMRT is convenient for patients and is particularly beneficial in busy radiotherapy department like in developing nations. Furthermore, PMRT can be given with or without bolus as per institution protocol. The purpose of this study was to do dosimetric comparison of with and without bolus plans in patient undergoing hypo fractionated PMRT and to assess acute toxicity of treatment.Methods: Our study is single institution prospective study done at DMCH cancer center Ludhiana, Punjab, India. Study period was from March 2020 to October 2020 and we included post mastectomy patients irradiated by hypo fractionated regime. After CT simulation and contouring, rapid arc radiotherapy plans were evaluated and DVH analysis was done for PTV and OARs. Acute toxicity was assessed during treatment and 1 month post radiotherapy treatment. Ethical approval was not taken due to COVID 19 pandemic emergency, but also hypofractionated PMRT is standard of care. Statistical analysis was done on SPSS, Version 20.0Results: A total of 30 patients were analyzed which received mean PTV dose of 42.3Gy in 16 fractions (8 fractions with and 8 without bolus).We were able to achieve adequate PTV coverage in plan sum which included both bolus and non-bolus plan. However, use of bolus resulted in statistically significant increase in low dose volume mainly V4Gy of ipsilateral lung in left sided breast cancer cases. Despite use of bolus no patient had above grade I skin toxicity.Conclusions: Moderate hypo fractionated PMRT with and without bolus is well tolerated with minimal acute side effects. It is important to note that use of bolus results in higher V4Gy volume of ipsilateral lung more precisely in left side breast cancer cases

Magnetic resonance spectroscopy — Revisiting the biochemical and molecular milieu of brain tumors

AbstractBackgroundMagnetic resonance spectroscopy (MRS) is an established tool for in-vivo evaluation of the biochemical basis of human diseases. On one hand, such lucid depiction of ‘live biochemistry’ helps one to decipher the true nature of the pathology while on the other hand one can track the response to therapy at sub-cellular level. Brain tumors have been an area of continuous interrogation and instigation for mankind. Evaluation of these lesions by MRS plays a crucial role in the two aspects of disease management described above.Scope of reviewPresented is an overview of the window provided by MRS into the biochemical aspects of brain tumors. We systematically visit each metabolite deciphered by MRS and discuss the role of deconvoluting the biochemical aspects of pathologies (here in context of brain tumors) in the disease management cycle. We further try to unify a radiologist's perspective of disease with that of a biochemist to prove the point that preclinical work is the mother of the treatment we provide at bedside as clinicians. Furthermore, an integrated approach by various scientific experts help resolve a query encountered in everyday practice.Major conclusionsMR spectroscopy is an integral tool for evaluation and systematic follow-up of brain tumors. A deeper understanding of this technology by a biochemist would help in a swift and more logical development of the technique while a close collaboration with radiologist would enable definitive application of the same.General significanceThe review aims at inciting closer ties between the two specialists enabling a deeper understanding of this valuable technology

New constraints on the up-quark valence distribution in the proton

The high-$x$ data from the ZEUS Collaboration are used to extract parton density distributions of the proton deep in the perturbative regime of QCD. The data primarily constrain the up-quark valence distribution and new results are presented on its $x$-dependence as well as on the momentum carried by the up-quark. The results were obtained using Bayesian analysis methods which can serve as a model for future parton density extractions.Comment: Minor changes in final published versio