Machine learning as a tool for identifying opportunities for intervention in a university module

Machine learning algorithms are used to analyze medical data sets effectively in present. Today machine learning gives us several necessary tools for intelligent data analysis and research. Especially in very recent years, the digital world has provided relatively inexpensive and available means to collect and store the data. The main aim is to implement supervised machine learning concept by using datasets regarding blood cells collected from blood cells detecting and counting sensors, of a human as the input which is trained by artificial neural network algorithm and apply decision tree classification learning algorithm to perform classification which results in recognizing and also possibly predict the disease based on the nature of blood cells and classify accordingly. Artificial Neural network algorithm seems to avoid pruning problem and has higher efficiency and accuracy in training the datasets. Also the using of Decision tree is because they are easy to interpret, understand and also possess non-linear characteristics between values. This holds well the performance of the tree constructed which gives better outputs. Beside all the application developed using machine learning in day today life, the use of such learning algorithm in such medical application will enhance and benefit the medical field.

Heart disease is one of the leading cause deaths of worldwide. Prediction of cardiovascular infection is a critical challenge in the area of clinical data analysis. Data mining techniques are providing an effective decision and significant results on data that are used widely for predicting. The purpose of this paper is to propose a novel approach with aims to find a noteworthy method to diagnose heart disease prediction. In this research, a unique dataset was created by combining the Cleveland dataset and Stalog heart disease datasets collected from the UCI ML repository. The new dataset contains 14 medical parameters such as age, sex, blood pressure, and 568 instances for training and prediction heart disease. This paper offers a novel methodology of NNDT (Neural Network and Decision Tree) that uses Neural Network for training model and Decision Tree to test classification for better heart disease prediction. The performance of the proposed approach have been compared with Naive Bayes, Support Vector Machine, Neural Network, Voted Perceptron, and Decision Tree algorithms. The results showed that the accuracy and performance improved as compared to other techniques and methods. This study enables the researchers to analyze the heart disease data with a new approach to predict heart diseases to maintain human health.

At present, patients whose have suffered from stroke in Thailand are increasing every year. Stroke impairments relate to many functions such as sensory, motor function, communication, visual and emotional function which depend on brain's lesion. Physical examinations and assessments are important for planning the rehabilitation programs. For this reason, there are several information for medical decision making. Missing some data for treatment planning may occur. To solve this problem, the proposed study used two algorithms to determine the proper rehabilitation treatment program. Artificial Neural Networks and Decision Trees models were considered. Sensitivity, specificity and accuracy values were computed to define the performance of both algorithms. The results of this study indicated that both techniques can apply for data classification and define the proper treatment programs. However, the results were shown that the specificity and accuracy of decision trees model were higher than neural network model.

Predictive systems use historical and other available data to predict an event. In this paper we tries to compare the power of Artificial Neural Network (ANN) and Decision Tree (DT) in prediction of aerology events with time series streams and events stream using combination of K-means clustering algorithm and Decision Tree C5 algorithm and ANN. We try to find the effective parameters on events occurrences. Firstly, we find the closest time series record for any events; therefore, we have gathered different parameters value when an event is occurring. Using K-means we add a field to dataset which determines the cluster of each record and after that we predict the events using C5 algorithm and ANN. This framework and time series model can predict future events efficiently. We gathered 1961 until 2005 data of aerology organization for Tehran Mehrabad Station. This data contains some fields such as wet bulb, relative humidity, amount of cloud, wind speed and etc. This dataset includes 17 types of events. Using this framework the closest event can be predicted. The C5 method is able to predict events with 79.55% accuracy and ANN with 72.87% accuracy. Applying K-means clustering algorithm the prediction increase to 94.59% for C5 and 92.66% accuracies for ANN. We use 10-fold cross validation to evaluate our prediction rate. This framework is the first estimation in the area of event prediction for a huge dataset of aerology and can be extended in many different datasets in any other environments.

Smart cities integrate data collection and communication technologies to operate more efficiently in order to provide better services to citizens, improved road traffic situation, better economic development, etc. However, safety still a main concern of road traffic as road crashes pose serious threats to road users causing death and injury all over the world. Road traffic safety is the process of implementing measures to reduce the number of crashes, death and severe injuries. In this paper, using Machine Learning (ML) algorithms, Off-line, we train and test the Intelligent and Dynamic Adaptation Model (IDAM) on the road traffic data collected for 14 years of car crashes in United Kingdom (UK). IDAM then on-line determines the optimal speed limit for each road segment based on many predictors such as weather, road type, light condition, and many others, stated later. We train and test IDAM using three different machine learning algorithms: Artificial Neural Network (ANN), Decision Tree (DT), and Linear Regression (LR) with Stochastic Gradient Descent (SGD). Using the testing dataset, IDAM achieves prediction accuracy of around 96%. Implemented in an operation algorithm, IDAM can continuously adapt the road speed limit to an optimal value as the predictors change on-line.

A Comparison of Predicting Movie Success between Artificial Neural Network and Decision Tree

In this article, the authors proposed the method of medical diagnosis in gestational diabetes mellitus (GDM) in the initial stages of pregnancy to facilitate diagnoses and prevent the affection. Nowadays, in industrial modern world with changing lifestyle alimental manner the incidence of complex disease has been increasingly grown. GDM is a chronic disease and one of the major health problems that is often diagnosed in middle or late period of pregnancy, when it is too late for prediction. If it is not treated, it will make serious complications and various side effects for mother and child. This article is designed for answering to the question of: “What is the best approach in timely and accurate prediction of GDM?” Thus, the artificial neural network and decision tree are proposed to reduce the amount of error and the level of accuracy in anticipating and improving the precision of prediction. The results illustrate that intelligent diagnosis systems can improve the quality of healthcare, timely prediction, prevention, and knowledge discovery in bioinformatics.

This paper takes one thousand records of some medical information system for example, makes data mining according to patients' data record about drug selection, uses BP neural network and decision tree method respectively to have model building and finds the best medicine fits for this kind of disease. The results of the experiment validate the method's feasibility and validity and provide reference frame to assist doctor to use medicine.

Nowadays, expert systems being used in various fields have received a great deal of attention. Auditing is one such field, along with determining the audit opinion type. An expert system consists of a knowledge database and an inference engine. The objective of this research is to make an expert system that will be of help to auditors in predicting and determining the different types of audit reports. The expert system receives data or knowledge from financial reports and determines the types of audit opinions by using an artificial neural network and a decision tree as an inference engine. An expert system should be able to explain the solution, but presenting the reason for the results obtained with a neural network is difficult. This study attempts to provide a method that will present simple and understandable reasons for the results obtained with neural networks.

In this paper we enhance the notion of anomaly detection and use both neural network (NN) and decision tree (DT) for intrusion detection. While DTs are highly successful in detecting known attacks, NNs are more interesting to detect new attacks. In our method we proposed a new approach to design the system using both DT and combination of unsupervised and supervised NN for Intrusion Detection System (IDS). By applying DT known attacks would be recognized with a quick execution time. Unknown attacks would be detected by applying the unsupervised NN based on hybrid of Self Organizing Map (SOM) for clustering attacks into smaller categories and supervised NN based on Backpropagation for detailed clustering.

In machine learning, the learning paradigms of an artificial neural network (ANN) and a decision tree (DT) are different, but they are equivalent in essence. This paper proves the approximate equivalency between feedback neural networks and decision trees. The result provides us a very useful guideline when we perform theoretical research and applications on DT and ANN.

In this paper a multimodal fingers classification to detect touch points over general interactive surfaces is presented. Three different classifiers have been used: artificial neural networks, decision trees and rules learner. The data set has been created extracting statistical parameters from finger ROIs on about 40000 video samples. The accuracy obtained for the three classifiers on the test set is respectively 96,68%, 96,58% and 97,41%. The model classifiers generated work very well in real-time applications, so an innovative software called TouchPAD has been designed and implemented.

The purpose of this retrospective study is to measure machine learning models' ability to predict glaucoma drainage device failure based on demographic information and preoperative measurements. The medical records of 165 patients were used. Potential predictors included the patients' race, age, sex, preoperative intraocular pressure (IOP), preoperative visual acuity, number of IOP-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final IOP greater than 18 mm Hg, reduction in intraocular pressure less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine. Recursive feature elimination was used to shrink the number of predictors and grid search was used to choose hyperparameters. To prevent leakage, nested cross-validation was used throughout. With a small amount of data, the best classfier was logistic regression, but with more data, the best classifier was the random forest.

The leading treads towards social networking services had drawn massive public attention from last one and half decade. The amount of data that is uploaded to these social networking services is increasing day by day. So, their is massive requirement to study the highly dynamic behavior of users towards these services. This is a preliminary work to model the user patterns and to study the effectiveness of machine learning predictive modeling approaches on leading social networking service Facebook. We modeled the user comment patters, over the posts on Facebook Pages and predicted that how many comments a post is expected to receive in next H hrs. To automate the process, we developed a software prototype consisting of the crawler, Information extractor, information processor and knowledge discovery module. We used Neural Networks and Decision Trees, predictive modeling techniques on different data-set variants and evaluated them under Hits@10(custom measure), Area Under Curve, Evaluation Time and Mean Absolute error evaluation metrics. We concluded that the Decision trees performed better than the Neural Networks under light of all evaluation metrics.

In manufacturing practices of most big printing companies there are collected data and records of process parameters. Gathering information from this data in form of developed models and rules such as data mining, which uses statistical methods or Artificial Intelligence, Artificial Neural Networks, Decision Trees, Expert Systems, and others are subjects of inter-disciplinary fields of science called Data Mining. In the shrink sleeve production process, the effects of using data mining tools not only improve the quality of the shrink sleeve and winding process but also reduce manufacturing costs. This paper describes how developed models of data mining tools can be used for prediction of initial tension parameters and winding speed for each every new design of shrink sleeve labels. Every one design of shrink sleeve label has a lot of factors. Some of them are more significant, some of them less. The aim of this paper is to choose significant factors and compute a models in learning process using collected data. Finally when models will be computed, can be used for prediction of key winding parameters of each new shrink sleeve label designs. This will bring for company saved time for experimental selection during converting of winding parameters like tension and speed and also will minimize risk of occurrence of defects with incorrect winding parameters.