AN EFFICIENT PREDICTIVE SYSTEM FOR HEART DISEASE USING A MACHINE LEARNING TRAINED MODEL

Heart is the most important organ of a human body. Through blood, it transfers oxygen and vital nutrients to the various parts of the body and helps in the metabolic activities and it removes wastes of metabolic. Thus, even minor problems in heart can affect the whole organism. Researchers are diverting a lot of data analysis work for assisting the doctors to predict the heart problem. So, an analysis of the data related to different health problems and its functioning can help in predicting with a certain probability for the wellness of this organ. In order to assist the physicians, identification of heart disease by machine learning and data mining technique has been implemented. Heart as one of the essential organ of the human body and with its related disease such as cardiovascular diseases accounts for the death of many in our society over the last decades, and also regarded as one of the most life-threatening diseases in the world. Today healthcare industry is rich in data however poor in knowledge. There are different data mining and tools and algorithms of ML are available for extraction of knowledge from data storeand to use this knowledge for more accurate diagnosis and decision making. The main contribution of this review is tosummarize the recent research with comparative results that has been done on heart disease prediction and also make analytical conclusions. From the study, it is observed Naive Bayes with Genetic algorithm; Decision Trees and ANN techniques enhance the accuracy in predicting heart disease in different scenarios

Phyto-pharmacognostic evaluation and HPLC study on Sariva (Hemidsmus indicus R.Br) Root

Plants are among the richest sources of bioactive compounds throughout the world for thousands of years and continue to provide new remedies to mankind. Roots of Hemidesmus indicus R. Br. is an important plant drug which is used to cure leprosy, leucoderma, itching, skin disease, asthma, bronchitis, leucorrhoea, dysentery, piles, syphilis, paralysis, urinary disorders and diabetes mellitus. As per Acharya Charaka has mentioned suitable season for collection of Moola i.e. Greeshma and Shishira Rutu. Here an attempt to study to check the potency of root collected as per classical reference. The present study focused on the pharmacognostical, phytochemical investigation as well as HPLC study were performed by taking different solvent extracts of Hemidesmus indicus root. This study highlights the detailed HPLC study on Hemidesmus indicus root by taking different solvent extracts with their increasing polarity which is a referential information for identification parameters and improves our confidence level of acceptability of herbal drugs

Shami (Prosopis cineraria (L) Druce) - A Medicinal Benison

Shami (Prosopis cineraria (L) Druce) belongs to family Fabaceae known for its spiritual uses in India mentioned in almost all the Nighantu’s of Ayurveda. It is endemic to Hot, Dry and Arid regions of India. Even though almost all the parts of Shami are having pharmacological actions specially the Bark and Fruit but these remain unexplored. It mainly contains tannins (gallic acid), alkaloids (spicigerine, prosophylline), Flavone derivatives (prosogerin A, B, C, D and E) and quercetin are widely used as anti-oxidant, anti-microbial, anti-bacterial, anti-convulsant, nootropic and antidepressant activity. Thus, the current article reviews on Ayurveda literature, botanical description, varieties and powder microscopy of Shami

GRIN1- Related neurodevelopmental disorder-Autism with Epilepsy - A case report

<p>GRIN1-related neurodevelopmental disorders are  a group of rare paediatric encephalopathies,with  estimated    prevalence    of    1:5000    births     (1) .Genetic variation in the GRIN1 gene have been  associated with a wide range of neurologic and  neuropsychiatric  disorders  .GRIN1  (Glutamate  Receptor   Inotropic,   NMDA   1)   is   the   name  of  the  gene  that  is  affected.</p&gt