RESUMEN
Background: Smoking has been found to have a profound effect on mortality and cause-specific cardiovascular events in hypertension with significant interactions between the effects of smoking and hypertension and diabetes. Nevertheless, smoking is a major modifiable risk factor for cardiovascular disease (CVD). Materials and Methods: The present study was conducted on the patients visiting the medical Out Patient Department, Government Medical College (OPD GMC), Anantnag, for consultation with an aim to find whether smoking can be linked with CVD as a cause. A total of 304 patients were observed during this period for health check-ups. Results: The result reveals that 90% of male subjects across the age groups formulated in this study were in habit of smoking. In addition, 75% of female subjects across all age groups were also found to be in the habit of smoking. The majority of subjects including male subjects were potentially susceptible to CVD. The present prospective study was carried out to assess the role of smoking in causing hypertension and thereby various CVDs among the south Kashmiri population with high blood pressure levels in presence of high smoking rates. Discussions: Smoking acutely exerts a hypertensive effect, mainly through the stimulation of the sympathetic nervous system. Chronic smoking affecting arterial stiffness and wave reflection has greater detrimental effect on central blood pressure, which is more closely related to target organ damage than brachial blood pressure. Hypertensive smokers are more likely to develop severe forms of hypertension, including malignant and renovascular hypertension, an effect likely due to accelerated atherosclerosis. Conclusion: Smoking is potentially a leading cause of CVD among the South Kashmiri Population with high blood pressure levels in presence of high-smoking rates. Therefore, imperative measures regarding cessation of smoking are essential to prevent CVD which in line with clinical practice guidelines and policies should be emphasized to treat nicotine addiction in smokers by incorporating multicomponent and multilevel approaches for the better management of BP among the population studied.
RESUMEN
Background: Diabetes mellitus (DM) is a chronic condition that can lead to a variety of consequences. Diabetes is a condition that is caused by factors such as age, lack of exercise, sedentary lifestyle, family history of diabetes, high blood pressure, depression and stress, poor food, and so on. Diabetics are at a higher risk of developing diseases such as heart disease, nerve damage (diabetic neuropathy), eye problems (diabetic retinopathy), kidney disease (diabetic nephropathy), stroke, and so on. According to the International Diabetes Federation, 382 million people worldwide suffer from diabetes. By 2035, this number will have risen to 592 million. Every day, a large number of people become victims, and many are ignorant whether they have it or not. It primarily affects individuals between the ages of 25 and 74 years. If diabetes is left untreated and undiagnosed, it can lead to a slew of complications. The emergence of machine learning approaches, on the other hand, solves this crucial issue. Aims and Objectives: The aim was to study the DM and analyze how machine learning algorithms are used to identify the diabetes mellitus at an early stage, which is one of the most serious metabolic disorders in the world today. Methods and Materials: Data was obtained from databases such as Pubmed, IEEE xplore, and INSPEC,and from other secondary sources and primary sources in which methods based on machine learning approaches used in healthcare to predict diabetes at an early stage are reported. Results: After surveying various research papers, it was found that machine learning classification algorithms like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) etc shows the best accuracy for predicting diabetes at an early stage. Conclusion: Early detection of diabetes is critical for effective therapy. Many people have no idea whether or not they have it. The full assessment of Machine learning approaches for early diabetes prediction and how to apply a variety of supervised and unsupervised machine learning algorithms to the dataset to achieve the best accuracy are addressed in this paper.. Furthermore, the work will be expanded and refined to create a more precise and general predictive model for diabetes risk prediction at an early stage. Different metrics can be used to assess performance and for accurate diabetic diagnosis.