The annual report of World Health Association, add up to the number of individuals experiencing diabetes is 422 million the year (Write from which year to which year it is telling the statistics of 422 million). Consistently, there is a significant increment in the number individuals experiencing diabetes in different healing center. The world health organization (WHO) reports [1, 2] on "Diabetes Care 2018" by American Diabetes Association and Standards for Medical care in Diabetes, a study for correlation diverse races and their pay. Figure 1 demonstrates the diverse individuals (gender and wage) matured between 29 and 70 years, level of passing because of hypertension. Diabetes mellitus [3] is chronic, a ceaseless ailment where it caused because of the high sugar level in the circulatory system. It is caused because of the inappropriate working of the pancreatic beta cells. It has an impact on different parts of the body which incorporates pancreas glitch, risk of heart ailments, hypertension, kidney disappointments, pancreatic issues, nerve harm, foot issues, ketoacidosis, visual unsettling influences, and other eye issues, waterfalls and glaucoma and so on. There are different purposes behind reason like a way of life of a man, the absence of activity, sustenance propensities, heftiness, smoking, high cholesterol (Hyperlipidaemia), high blood pressure Abstract Diabetes is a chronic disease or group of metabolic disease where a person suffers from an extended level of blood glucose in the body, which is either the insulin production is inadequate, or because the body's cells do not respond properly to insulin. The constant hyperglycemia of diabetes is related to long-haul harm, brokenness, and failure of various organs, particularly the eyes, kidneys, nerves, heart, and veins. The objective of this research is to make use of significant features, design a prediction algorithm using Machine learning and find the optimal classifier to give the closest result comparing to clinical outcomes. The proposed method aims to focus on selecting the attributes that ail in early detection of Diabetes Miletus using Predictive analysis. The result shows the decision tree algorithm and the Random forest has the highest specificity of 98.20% and 98.00%, respectively holds best for the analysis of diabetic data. Naïve Bayesian outcome states the best accuracy of 82.30%. The research also generalizes the selection of optimal features from dataset to improve the classification accuracy.
A noncontact method to quantify the topography and aberrations of corneal surfaces with OCT was presented. OCT measurements yielded greater curvature and aberrations than Pentacam in both normal and keratoconic eyes. [J Refract Surg. 2017;33(5):330-336.].
Background Radiotherapy is frequently used to treat head and neck Squamous cell carcinomas (HNSCC). Treatment outcomes being highly uncertain, there is a significant need for robust predictive tools to improvise treatment decision-making and better understand HNSCC by recognizing hidden patterns in data. We conducted this study to identify if Machine Learning (ML) could accurately predict outcomes and identify new prognostic variables in HNSCC. Method Retrospective data of 311 HNSCC patients treated with radiotherapy between 2013 and 2018 at our center and having a follow-up of at least three months' duration were collected. Binary-classification prediction models were developed for: Choice of Initial Treatment, Residual disease, Locoregional Recurrence, Distant Recurrence, and Development of New Primary. Clinical data were pre-processed using Imputation, Feature selection, Minority Oversampling, and Feature scaling algorithms. A method to retain original characteristics of dataset in testing samples while performing minority oversampling is illustrated. The classification comparison was performed using Random Forest (RF), Kernel Support Vector Machine (KSVM), and XGBoost classification algorithms for each model. Results For the choice of the initial treatment model, the testing accuracy was 84.58% using RF. The distant recurrence, locoregional recurrence, new-primary, and residual models had a testing accuracy (using KSVM) of 95.12%, 77.55%, 98.61%, and 92.25%, respectively. The important clinical determinants were identified using Shapely Values for each classification model, and the mean area under the curve (AUC) for the receiver operating curve was plotted. Conclusion ML was able to predict several clinically relevant outcomes, and with additional clinical validation, could facilitate recognition of novel prognostic factors in HNSCC.
Background Radiomics involves the extraction of quantitative information from annotated Computed-Tomography (CT) images, and has been used to predict outcomes in Head and Neck Squamous Cell Carcinoma (HNSCC). Subjecting combined Radiomics and Clinical features to Machine Learning (ML) could offer better predictions of clinical outcomes. This study is a comparative performance analysis of ML models with Clinical, Radiomics, and Clinico-Radiomic datasets for predicting four outcomes of HNSCC treated with Curative Radiation Therapy (RT): Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease. Methodology The study used retrospective data of 311 HNSCC patients treated with radiotherapy between 2013–2018 at our centre. Binary prediction models were developed for the four outcomes with Clinical-only, Clinico-Radiomic, and Radiomics-only datasets, using three different ML classification algorithms namely, Random Forest (RF), Kernel Support Vector Machine (KSVM), and XGBoost. The best-performing ML algorithms of the three dataset groups was then compared. Results The Clinico-Radiomic dataset using KSVM classifier provided the best prediction. Predicted mean testing accuracy for Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease was 97%, 72%, 99%, and 96%, respectively. The mean area under the receiver operating curve (AUC) was calculated and displayed for all the models using three dataset groups. Conclusion Clinico-Radiomic dataset improved the predictive ability of ML models over clinical features alone, while models built using Radiomics performed poorly. Radiomics data could therefore effectively supplement clinical data in predicting outcomes.
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