European Journal of Molecular & Clinical Medicine

Diagnosis is an area of utmost importance in medical treatment. A person can be cured only when the doctor properly diagnoses the disease and gives the appropriate treatment. However, wrong diagnosis, wrong treatment even when the disease is correctly diagnosed, and wrong treatment can cause side effects and delay the cure. Sometimes it can be life-threatening. It is important for us to know how allopathic doctors, who are now called modern doctors, diagnose disease. They first note down the patient's complaints in order. In this paper, an innovation development of disease identification was proposed using medical deep learning model. The complete and correct information they can provide helps in proper diagnosis. Only after that the doctors examine the patient's body. Testing is not just about checking pulse and blood pressure. All body parts will be examined like abdomen, nervous system-brain function, muscle, skeletal system. Urinary tract and sexual organs will also be examined by the appropriate doctor. Thus, after doing a full body examination, they find out what the patient is suffering from.

Fetal growth restriction (FGR) is a pathological condition in which a fetus has not achieved his genetic growth potential, regardless of fetal size (1) Worldwide FGR is observed in about 24% of newborns; approximately 30million infants suffer from FGR every year. The burden of FGR is concentrated mainly in Asia which accounts for nearly 75% of all affected infants. National neonatal perinatal database of India reported the incidence of FGR to be 9.65% among hospital born live birth infants. Study was conducted for all cases with clinical/ Sonological term FGR admitted under department of OBG. A detailed history as per questioner will be taken with general physical examination and investigations will be done as per requirement. The accumulated data was evaluated and statistically analyzed. In the present study 70 patients with term gestation with FGR were recruited. Maternal (74.28%) was the commonest cause followed by Idiopathic (11.43) and Placental (10%) and Fetal (4.29%) causes. Among Maternal causes Pre Eclampsia was found to be in 50% cases. Most of the patients (50.7%) required caesarean section. A total of 9 (12.86%) neonate had birth weight of <1.5 kg, 48.6% had Birth weight between 1.6 to 1.9kg, 38.5% had birth weight between 2-2.4kg and 95.8% had asymmetrical FGR, 4.2% were symmetrical. 26 (40%) neonates had morbidity with 17(24.3%) neonatal mortality with Respiratory distress syndrome (41.18%) being most common cause. No Maternal Mortality.

Abstract: Back pain is a syndrome that can be associated with degenerative-dystrophic
changes in the spine, muscle damage and diseases, damage to the nervous system (the spinal
cord, its roots, and peripheral nerves), pathology of the internal organs of the thoracic and
abdominal cavities, pelvis, and mental disorders. The most common cause of acute back pain
is changes that occur when the muscles, ligaments, or joints of the spine are overloaded

The epidemic of Covid-19 has created a disastrous situation around the globe. The spread of Covid-19 is drastically increasing day by day. Machine learning is one of the efficient tools to track the outbreak of the disease, forecast the probable confirmed and death cases as well as the fatality rate. This study applies multiple regression analysis which is one of the supervised machine learning algorithms to analyze and forecast the fatality rate. The study was conducted to predict the spread of Covid-19 in areas of Telangana, Andhra Pradesh, and India. R-Square (R2), Mean square error (MSE), Root mean square error (RSME) and Mean absolute error (MAE) are the main measures used to predict the accuracy of the algorithm. The results reveal that the case fatality rate is higher in Telangana compared to Andhra Pradesh and India, and more diseased cases are observed in Andhra Pradesh. The study was conducted with the available data; if sufficient data is available then the more precise predictions could be possible using multiple regression analysis.

the solution of the problem lies in determining the integral indicator of the load of risk factors for generalized periodontitis, which takes into account the prevalence of risk factors and their contribution to the occurrence of the disease.
The purpose of this study is to analyze the population determinants of the risk of developing generalized periodontitis

Metabolic syndrome is a group of simultaneous conditions that expanding your risk of heart disease, stroke, and type 2 diabetes. These conditions include increased blood pressure, high glucose level, and abundance muscle versus fat around the abdomen or triglyceride levels. The objective of the study is to demographic, biochemical, obesity indices and angiographic profile (severity of CAD) of patients with coronary artery disease (CAD). A prospective and observational study involving a number of 971 patients who had undergone coronary angiogram (CAG) for the 18-month CAD assessment. In present study about half of population had significant abnormality on coronary angiogram. Amongst abnormal coronary angiogram about 50% of patients had single vessel disease. The FRS had positive correlation with severity of coronary artery disease and waist circumference. FRS and metabolic syndrome had critical contribution as score and risk factors with presence of and severity of CAD. To conclude it was suggested to incorporate FRS and components of metabolic syndrome for better management and risk stratification of coronary artery disease at large.