Online ISSN: 2515-8260

Keywords : Prediction


Analyzing Diabetic Data Using Naive-Bayes Classifier

A. Sharmila Agnal; E. Saraswathi

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2687-2699

Approximately 422 million people across the world have diabetes, particularly in countries where the average income is in the middle and lower end of the economic spectrum. Statistics reveal that every year, about 1.6 million deaths are recorded which can be directly attributed to diabetes. The graph suggests that number of cases as well as the prevalence of diabetes have been steadily incrementing over the past few decades. Through this new implementation of the Bayesian Classifier, raw medical data is analyzed and the risk of diabetes diagnosis based on each patient’s medical information can be calculated. The raw data is converted into class labels and the likelihood of a positive potential diabetes case is derived, as a probability (≤1). This can not only be used by healthcare professionals but also by common users, and can be useful in detecting the risk and preventing it in time without taking any medical tests. This classifier uses very basic information that would be known to each patient or can easily be obtained.

Diabetes Data Prediction in healthcare Using Hadoop over Big Data

Gajanand Sharma; Ashutosh Kumar; Himanshu Sharma; Ashok Kumar Saini; Priyanka .; S.R. Dogiwal

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1423-1432

Diabetes mellitus is one of the major non-communicable diseases which have great impact on human life today. A huge amount of data is generated including a wide variety of the Electronic Medical Record (EMR), pharmacy reports, and laboratory reports, among other data related to patients. Big data analytics can be applied to this data to generate useful patterns and relation between different factors which affects diabetes. The results obtained from this analysis shows relation between different attributes which can be used to improve healthcare system. In this paper the analysis of the diabetes dataset is done using Hadoop framework, which is a distributive framework and can be used to analysis large amount of data. The dataset is taken from PIMA Indian Database, which includes different factors that affect diabetes like age, blood pressure, BMI (Body-Mass Index), skin thickness etc. Results produced by the analysis of data are projects on Power BI.

AN OVERVIEW OF THE METHODS OF PREDICTION PLANNING FOR ORTHOGNATHIC SURGERY USING CEPHALOMETRICS

Dr. Niha Naveed; Dr. Kannan Sabapathy

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1675-1685

Prediction planning for orthognathic surgery allows the orthodontist to anticipate changes in hard and soft tissue that may arise as a result of the surgery. This can be useful to accordingly plan the orthognathic surgery and also as a means for informed patient’s consent and to communicate with the concerned maxillofacial surgeon. Cephalometric prediction in orthognathic surgery enables direct evaluation of both dental and skeletal movements, and can be performed manually or by computers, using several software programmes currently available. They can also be incorporated with video images. The aim of this article is to present and discuss the different methods of cephalometric prediction of the orthognathic surgical outcome.

Prediction of the Crop Cultivating using Resembling and IoT Techniques in Agricultural Fields for Increasing Productivity

Anant Ram; Rakesh Kumar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 50-53

The agriculture plays a prevailing job in the development of the nation's economy. Atmosphere and other natural changes has become a significant danger in the agribusiness field. AI is a fundamental methodology for accomplishing viable and viable answers for this issue. Harvest Prediction includes anticipating the best output from accessible authentic information like climate parameters and soil parameters. This recommender system uses real time data as input to the machine learning. The sensors collect data from the soil and send that data to the cloud (firebase). Then the machine learning model retrieves that data and predicts the best crop and sends that crop to the cloud. We develop an android application which retrieves the sensor values from the cloud and displays them. This forecasting facilitates the farmer to forecast the best crop earlier than cultivating onto the agriculture field, which in turn increases the productivity.