European Journal of Molecular & Clinical Medicine

Non-Alcoholic Fatty Liver Disease (NAFLD) is an important cause of liver disease in India. Epidemiological studies suggest its prevalence in around 9% to 32% of general Indian population, but with a higher prevalence in those having overweight / obesity and diabetes. Present study aimed to investigate the usefulness of ‘Fatty Liver Index’ an algorithm to predict the presence of Non Alcoholic Fatty Liver Disease among the Population visiting the Master Health Check-up of a tertiary care centre.

Customer complexity is a main issue and for large companies is the main problem. Considering the immediate impact on firms ’earnings, companies are trying to change strategies to calculate customer concerns. Consequently, it is very important to find a way to solve this problem by differentiating the factors that increase the client's depression. The chief involvement of this study is to progress an effective churnprediction prototypical using a hybrid approach. Here, initially, data is collected from the dataset and the missing data is removed at the pre-processing stage. Then, to reduce the problem, the input dataset is enhanced as a dimension reduction function. For dimensional reduction, the proposed method uses a hybrid technique. Here, PCA and LDA algorithm are hybridized to reduce dimensionality. After the dimensionality reduction process, the reduced dataset is provided to the optimal continuous neural network (ORNN). Here, the traditional RNA classifier is trained with Cat Swarm Optimization (CSO). In this work, Tera Data Center at Duke University churn set of predictive data for the calculation, the measured performance. Finally, the performance of the proposed model is estimated at different scales, and it is recognized that the proposed system, designed with dimensional reduction through optimal classification methods, performs better with 95.08% classification accuracy compared to other classification models.

The paper considers the possibility of predicting tumor parameters (weight, volume) depending on the method of treatment and doses of drugs used. A comparison is made of the applicability of several regression models, taking into account the limited amount of data. Brown's adaptive model, paired regression, multiple regression are compared. Previously, all constructed regression models were tested for adequacy by assessing the significance of the coefficient of determination according to Fisher's criterion, and only the models that were recognized as adequate were used for further predictions. The advantage of Brown's model on the considered limited data set is shown experimentally and recommendations are given on the choice of the method parameters.

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.

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.

With the rapid growth of COVID-19 pandemic infectious disease caused by the Corona Virus. It was first identified in Wuhan in December 2019. It expanded its circle all over the world and finally spreading its route to India. The whole world is fighting against the spread of this deadly disease, cases in India also gradually increasing day by day since May after lockdown. This article proposes how to contribute to utilizing the machine learning and deep learning models with the aim for understanding its everyday exponential behaviour along with the prediction of future reachability of the COVID-2019 across the nations by utilizing the real-time information from the Johns Hopkins. This paper studies the COVID-19 dataset and explore the data by data visualization with different libraries that are available in Python. The paper also discusses the current situation in India while tackling the Covid-19 pandemic and the ongoing development in AI and ML has significantly improved treatment, medication, screening tests, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark. Within this paper, we present Exploratory Data Analysis, Data Preprocessing, Data Cleaning and Manipulations, Machine Learning Algorithms, Pandemic Analyzing Engine GUI, and Deep Learning. We have performed linear regression, Decision Tree, SVM, Random Forest and for forecasting, we performed FBPrompet, ARIMA model to predict the next 15 day’s Pandemic situation.

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.

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.