Early diagnosis of Alzheimer's disease is a challenge because the existing methodologies do not identify the patients in their preclinical stage, which can last up to a decade prior to the onset of clinical symptoms. Several research studies demonstrate the potential of cerebrospinal fluid biomarkers, amyloid beta 1-42, T-tau, and P-tau, in early diagnosis of Alzheimer's disease stages. In this work, we used machine learning models to classify different stages of Alzheimer's disease based on the cerebrospinal fluid biomarker levels alone. An electronic health record of patients from the National Alzheimer's Coordinating Centre database was analyzed and the patients were subdivided based on mini-mental state scores and clinical dementia ratings. Statistical and correlation analyses were performed to identify significant differences between the Alzheimer's stages. Afterward, machine learning classifiers including K-Nearest Neighbors, Ensemble Boosted Tree, Ensemble Bagged Tree, Support Vector Machine, Logistic Regression, and Naïve Bayes classifiers were employed to classify the Alzheimer's disease stages. The results demonstrate that Ensemble Boosted Tree (84.4%) and Logistic Regression (73.4%) provide the highest accuracy for binary classification, while Ensemble Bagged Tree (75.4%) demonstrates better accuracy for multiclassification. The findings from this research are expected to help clinicians in making an informed decision regarding the early diagnosis of Alzheimer's from the cerebrospinal fluid biomarkers alone, monitoring of the disease progression, and implementation of appropriate intervention measures.

Welcome to my new article series: Boosting algorithms in machine learning! This is Part 1 of the series. Here, I'll give you a short introduction to boosting, its objective, some key definitions and a list of boosting algorithms that we intend to cover in the next posts. You should be familiar with elementary tree-based machine learning models such as decision trees and random forests. In addition to that, it is recommended to have good knowledge of Python and its Scikit-learn library.

Multi-task learning (MTL) aims at improving the generalization performance of several related tasks by leveraging useful information contained in them. However, in industrial scenarios, interpretability is always demanded, and the data of different tasks may be in heterogeneous domains, making the existing methods unsuitable or unsatisfactory. In this paper, following the philosophy of boosted tree, we proposed a two-stage method. In stage one, a common model is built to learn the commonalities using the common features of all instances. Different from the training of conventional boosted tree model, we proposed a regularization strategy and an early-stopping mechanism to optimize the multi-task learning process. In stage two, started by fitting the residual error of the common model, a specific model is constructed with the task-specific instances to further boost the performance. Experiments on both benchmark and real-world datasets validate the effectiveness of the proposed method. What's more, interpretability can be naturally obtained from the tree based method, satisfying the industrial needs.

The Infant Mortality Rate (IMR) is the number of infants per 1000 that do not survive until their first birthday. It is an important metric providing information about infant health but it also measures the society's general health status. Despite the high level of prosperity in the U.S.A., the country's IMR is higher than that of many other developed countries. Additionally, the U.S.A. exhibits persistent inequalities in the IMR across different racial and ethnic groups. In this paper, we study the infant mortality prediction using features extracted from birth certificates. We are interested in training classification models to decide whether an infant will survive or not. We focus on exploring and understanding the importance of features in subsets of the population; we compare models trained for individual races to general models. Our evaluation shows that our methodology outperforms standard classification methods used by epidemiology researchers.

In order to easily automate the use of BigML's Machine Learning resources, we maintain a set of bindings, which allow users to work with the platform in their favorite language. Currently, there are 9 bindings for popular languages like Java, C#, Objective C, PHP or Swift. In addition, last year we released WhizzML to help developers create sophisticated Machine Learning workflows and execute them entirely in the cloud thus avoiding network problems, memory issues or lack of computing capacity, while taking full advantage of WhizzML's built in parallelization. In the past, we wrote about using WhizzML to perform Gradient Boosting and now we are making it even easier to perform with our Winter 2017 release.

BigML is bringing Boosted Trees to our ever-growing suite of supervised learning techniques. Boosting is a variation on ensembles that aims to reduce bias, potentially leading to better performance than Bagging or Random Decision Forests. In our first blog post of this series of six posts about Boosted Trees, we saw a gentle introduction to Boosted Trees to get some context about what this new resource is and how it can help you solve your classification and regression problems. This post will take us further, into the detailed steps of how to use boosting with BigML. To learn from our data, we must first upload it.

Based on different understandings of \( y_i \) we can have different problems, such as regression, classification, ordering, etc. We need to find a way to find the best parameters given the training data. In order to do so, we need to define a so-called objective function, to measure the performance of the model given a certain set of parameters. A very important fact about objective functions is they must always contain two parts: training loss and regularization. The training loss measures how predictive our model is on training data.