Nowadays, heart disease is the leading cause of death globally, with an estimated 610000 lives each year due to heart condition. One of the most common causes of heart disease is high blood pressure (HBP), fasting blood sugar (FBS), diabetes, cholesterol, Body mass Index (BMI), heart rate (HR). Diagnosis of heart disease is more prevalent nowadays; this involves a lot of accuracy and uncertainty due to the large-scale data decision-based on doctors may fail in some cases. Data mining is an intelligent diagnostic tool in healthcare. Thus, it is imperative to predict that each menace stage depends on age, sex, blood pressure, diabetes symptoms, what we can do for precaution by diagnosing the disease and proper treatment at the right moment. The purpose of the research work is to develop different predictive models using different forecasting measures and perform comparative analysis. In this work, we have used Cleveland and Statlog datasets with Naive Bayes (NB), K-Nearest Neighbors (KNN), and Logistic Regression (LR), Support vector machine (SVM), Decision Tree (DT), and Random Forest (RF) Classifier to develop various predictors. The experiment result shows that the random forest classifier gives better accuracy and results on both datasets when we perform a comparative analysis of them among all other classification models
Introduction: Human Immunodeficiency Virus (HIV) infection is a growing concern in paediatric population and large number of children are registered and treated at Antiretroviral Treatment (ART) centres across the country. Children with HIV progress more rapidly, develop more bacterial infections, suffer from neurologic developmental problems and have higher mortality than adults. So the screening and counselling of HIV positive parents and their children must be done timely. This helps the physician for starting the treatment timely. Aim: To determine the clinico-immunological profile of paediatric patients registered in ART, and to compare the immunological profile and clinical staging of paediatric patients receiving ART. Materials and Methods: This prospective longitudinal study was conducted in the ART centre of Sarojini Naidu Medical College, Agra, Uttar Pradesh, India, from October 2017 to October 2018. Total 51 children, upto the age group of 18 years, suffering from HIV/Acquired immunodeficiency syndrome (AIDS) were enrolled in the study. Diagnosis of HIV was confirmed using EnzymeLinked Immunosorbent Assay (ELISA) method (using two different antigens Comb HIV test, TRI-DOT) in children more than 18 months of age. In children less than 18 months age, diagnosis was confirmed using Deoxyribonucleic Acid (DNA) Polymerase Chain Reaction (PCR) (repeated twice with cessation of breast feeding for minimum of six weeks). Statistical analysis was done by using Statistical Package for the Social Sciences (SPSS) trial version 23.0 and simple frequency and Chi-square test was used for analysis. Results: Majority of the children (20, 39.26%) were in the age group of 5-10 years, and male: female ratio was 2.4:1. Most common presenting complaint was cough (52.94%), followed by fever (47.05%), chronic diarrhea (37.25%). Most common clinical signs seen were hepatosplenomegaly (41.17%), pneumonia (33.33%) and lymphadenopathy (31.37%). Initially the mean CD4 count was 370.31±231.5 cell/mm3 , and after starting ART mean CD4 count was 524.6±260.4 cell/mm3 . Significant improvement in CD4 count was observed in age group of 5-10 years (p-value=0.009), and 10-15 years (p-value=0.001) after six months of starting the ART. In the beginning, maximum (56.7%) children belonged to World Health Organization (WHO) clinical staging III and after starting ART maximum 40% belonged to stage II. Conclusion: ART improves symptomatology and immunological status HIV infected children, so there is need to screen the children of HIV affected parents and identify the children suffering with HIV in order to initiate ART at the earliest indication in order to improve their general health, freedom from illness and better immunological status.
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