I will show you how to apply Machine Learning algorithms on data from the PostgreSQL database to get insights and predictions. I will use an Automated Machine Learning (AutoML) supervised. It is an open-source python package. Thanks to AutoML I will get quick access to many ML algorithms: Decision Tree, Logistic Regression, Random Forest, Xgboost, Neural Network. The AutoML will handle feature engineering as well.

Let's say you want to take one of the very important decision in your life, it will be a choosing your career or choosing your life partner. Do you think that you can depend on a just one person advice. Advice from the one person can be highly biased also. The best way you can go ahead by asking and taking guidance from multiple people which reduce the bias. Same thing apply on machine learning world also while predicting some class or predicting any continuous value for regression problem, why you should rely on a one model only.

Weekly outpatient MRI appointment no-show rates for 1 year before (19.3%) and 6 months after (15.9%) implementation of intervention measures in March 2019, as guided by XGBoost prediction model. September 10, 2020 -- According to ARRS' American Journal of Roentgenology (AJR), artificial intelligence (AI) predictive analytics performed moderately well in solving complex multifactorial operational problems -- outpatient MRI appointment no-shows, especially -- using a modest amount of data and basic feature engineering. "Such data may be readily retrievable from frontline information technology systems commonly used in most hospital radiology departments, and they can be readily incorporated into routine workflow practice to improve the efficiency and quality of health care delivery," wrote lead author Le Roy Chong of Singapore's Changi General Hospital. To train and validate their model, Chong and colleagues extracted records of 32,957 outpatient MRI appointments scheduled between January 2016 and December 2018 from their institution's radiology information system, while acquiring a further holdout test set of 1,080 records from January 2019. Overall, the no-show rate was 17.4%.

Road accidents constitute a significant proportion of the number of serious injuries reported every year. Yet, it is often challenging to determine which specific conditions lead to such events, making it more difficult for local law enforcement to address the number and severity of road accidents. We all know that some characteristics of vehicles and the surroundings play a key role (engine capacity, condition of the road, etc.). However, many questions are still open. Which of these factors are the leading ones?

According to ARRS' American Journal of Roentgenology (AJR), artificial intelligence (AI) predictive analytics performed moderately well in solving complex multifactorial operational problems--outpatient MRI appointment no-shows, especially--using a modest amount of data and basic feature engineering. "Such data may be readily retrievable from frontline information technology systems commonly used in most hospital radiology departments, and they can be readily incorporated into routine workflow practice to improve the efficiency and quality of health care delivery," wrote lead author Le Roy Chong of Singapore's Changi General Hospital. To train and validate their model, Chong and colleagues extracted records of 32,957 outpatient MRI appointments scheduled between January 2016 and December 2018 from their institution's radiology information system, while acquiring a further holdout test set of 1,080 records from January 2019. Overall, the no-show rate was 17.4%. After evaluating various machine learning predictive models developed with widely used open-source software tools, Chong and team deployed a decision tree-based ensemble algorithm that uses a gradient boosting framework: XGBoost, version 0.80 [Tianqi Chen].

Bias takes many different forms and impact all groups of people. It can range from implicit to explicit and is often very difficult to detect. In the field of machine learning bias is often subtle and hard to identify, let alone solve. Why is this a problem? Implicit bias in machine learning has very real consequences including denial of a loan, a lengthier prison sentence, and many other harmful outcomes for underprivileged groups.

In machine learning, we mainly deal with two kinds of problems that are classification and regression. There are several different types of algorithms for both tasks. But we need to pick that algorithm whose performance is good on the respective data. Ensemble methods like Random Forest, Decision Tree, XGboost algorithms have shown very good results when we talk about classification. These algorithms give high accuracy at fast speed.

With the current pandemic spreading like wildfire, the requirement for a faster diagnosis can not be more critical than now. As a matter of fact, the traditional real-time polymerase chain reaction testing (RT-PCR) using the nose and throat swab has not only been termed to have limited sensitivity but also time-consuming for operational reasons. Thus, to expedite the process of COVID-19 diagnosis, researchers from the University of Oxford developed two early-detection AI models leveraging the routine data collected from clinical reports. In a recent paper, the Oxford researchers revealed the two AI models and highlighted its effectiveness in screening the virus in patients coming for checkups to the hospital -- for an emergency checkup or for admitting in the hospital. To validate these real-time prediction models, researchers used primary clinical data, including lab tests of the patients, their vital signs and their blood reports.

Antiobiotics adsorption on carbon-based materials was modeled by machine learning. Random forest showed best prediction accuracy than GBT and ANN. Impact tendencies of SBET, pHsol, C0 on adsorption were similar for TC and SMX. Chemical compositions and pHpzc of CBMs showed different influences on TC and SMX. Antibiotics as emerging pollutants have attracted extensive attention due to their ecotoxicity and persistence in the environment.

AdaBoost Algorithm is a boosting method that works by combining weak learners into strong learners. A good way for a prediction model to correct its predecessor is to give more attention to the training samples where the predecessor did not fit well. This can result in a new prediction model which will focus much on the hard instances. This technique is used by an AdaBoost Algorithm. In this article, I will take you through the AdaBoost Algorithm in Machine Learning.