scholarly journals Cost-Sensitive Machine Learning Classification for Mass Tuberculosis Screening

2019 ◽  
Author(s):  
Ali Akbar Septiandri ◽  
Aditiawarman ◽  
Roy Tjiong ◽  
Erlina Burhan ◽  
Anuraj H. Shankar
Keyword(s):  
Machine Learning ◽  
Tuberculosis Screening ◽  
Case Identification ◽  
Machine Learning Classification ◽  
Primary Health Centres ◽  
Positive Class ◽  
Data Points ◽  
The Difference ◽  
The Cost ◽  
And Control

AbstractActive screening for Tuberculosis (TB) is needed to optimize detection and treatment. However, current algorithms for verbal screening perform poorly, causing misclassification that leads to missed cases and unnecessary and costly laboratory tests for false positives. We investigated the role of machine learning to improve the predefined one-size-fits-all algorithm used for scoring the verbal screening questionnaire. We present a cost-sensitive machine learning classification for mass tuberculosis screening. We compared score-based classification defined by clinicians to machine learning classification such as SVM-RBF, logistic regression, and XGBoost. We restricted our analyses to data from adults, the population most affected by TB, and investigated the difference between untuned and unweighted classifiers to the cost-sensitive ones. Predictions were compared with the corresponding GeneXpert MTB/Rif results. After adjusting the weight of the positive class to 40 for XGBoost, we achieved 96.64% sensitivity and 35.06% specificity. As such, sensitivity of our identifier increased by 1.26% while specificity increased by 13.19% in absolute value compared to the traditional score-based method defined by our clinicians. Our approach further demonstrated that only 2000 data points were sufficient to enable the model to converge. Our results indicate that even with limited data we can actually devise a better method to identify TB suspects from verbal screening. This approach may be a stepping stone towards more effective TB case identification, especially in primary health centres, and foster better detection and control of TB.

scholarly journals Smooth input preparation for quantum and quantum-inspired machine learning

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhikuan Zhao ◽  
Jack K. Fitzsimons ◽  
Patrick Rebentrost ◽  
Vedran Dunjko ◽  
Joseph F. Fitzsimons
Keyword(s):  
Machine Learning ◽  
Quantum Algorithms ◽  
Machine Learning Algorithms ◽  
Low Rank ◽  
Smoothed Analysis ◽  
Machine Learning Applications ◽  
Data Points ◽  
Input Model ◽  
The Cost ◽  
Input Perturbation

AbstractMachine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoothed analysis that if the data analysis algorithm is robust against small entry-wise input perturbation, state preparation can always be achieved with constant queries. This criterion is typically satisfied in realistic machine learning applications, where input data is subjective to moderate noise. Our results are equally applicable to the recent seminal progress in quantum-inspired algorithms, where specially constructed databases suffice for polylogarithmic classical algorithm in low-rank cases. The consequence of our finding is that for the purpose of practical machine learning, polylogarithmic processing time is possible under a general and flexible input model with quantum algorithms or quantum-inspired classical algorithms in the low-rank cases.

scholarly journals How to Allocate the Allowance for the Aviation Industry in China’s Emissions Trading System

2019 ◽  
Vol 11 (9) ◽  
pp. 2541
Author(s):  
Bao-Jun Tang ◽  
Yu-Jie Hu
Keyword(s):  
Emissions Trading ◽  
Ghg Emissions ◽  
Civil Aviation ◽  
Aviation Industry ◽  
Trading System ◽  
The Difference ◽  
Combat Climate Change ◽  
The Cost ◽  
And Control ◽  
Emissions Trading System

In order to combat climate change and control emissions in the aviation industry, it is necessary to research the aviation industry’s potential application of China’s Emissions Trading System (ETS), especially the carbon allowance allocation (CAA). On the basis of historical and benchmarking CAA schemes, considering the responsibility, capacity, and potential of firms, this study proposes the indicators CAA (ICAA) scheme. Moreover, considering firms’ costs, this study also proposes a multi-objective CAA (MCAA) scheme. Finally, the most effective scheme is reported. Results show that under ICAA and MCAA, caps are lower and basically consistent with the emissions reduction target of the “13th Five-Year Plan Work Program for Controlling GHG Emissions of Civil Aviation in China” and international goals. Different types of airlines gain different quotas according to their income and the number and age of their aircraft. The cost of reducing emissions in each scheme is less than 0.35% of their total costs. Under the ICAA-S, ICAA-P, and MCAA schemes, airlines can achieve a reduction in emissions of 19.7%, 20.9%, and 19.6%, respectively. Moreover, under MCAA, the difference in quotas between airlines is smaller. Therefore, of the schemes evaluated, MCAA is the most effective.

scholarly journals Sentiment Analysis for Customer Opinion on Hotel using Machine Learning Techniques

2019 ◽  
Vol 8 (12) ◽  
pp. 5211-5213
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Computational Study ◽  
Machine Learning Techniques ◽  
Main Task ◽  
Product Portfolio ◽  
Machine Learning Classification ◽  
The Difference ◽  
Clear Idea

Opinions from others play a significant part to take our own decision, The people’s opinions, attitudes and emotions are a computational study toward an entity is called as Sentiment Analysis (SA) or Opinion Mining (OM). In today's world, everything like business, organization and even individuals wants to know opinion from public or customers about their presentation, products and about their services which will give clear idea about their product, portfolio in the market and if these services is not up to the mark how their services they improve, so that their business will perform better. To give output as positive, negative or neutral and find the difference of a specified user text or data from the dataset is the main task of the sentiment or opinion analysis. The opinions, sentiments and subjectivity of text are computational treatment in text mining with Sentiment Analysis (SA). With the help of sentiment analysis this paper describe the machine learning classification techniques for hotel reviews for which dataset obtained from Trip advisor hotel reviews website. System got 99.07 % accuracy for MAXENT Classifier with Train size and Test size 80% and 20% respectively.

scholarly journals The Vista of Application of Specific Anaphylaxis Accurate Diagnosis Based on DNA Single-Nucleotide Methylation Sites

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiangjie Guo ◽  
Yaqin Bai ◽  
Hualin Guo ◽  
Peng Wu ◽  
Hao Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Pollen Season ◽  
Accurate Diagnosis ◽  
The Other ◽  
Single Nucleotide ◽  
Machine Learning Classification ◽  
The World ◽  
On Line ◽  
The Difference

Anaphylaxis has rapidly spread around the world in the last several decades. Environmental factors seem to play a major role, and epigenetic marks, especially DNA methylation, get more attention. We discussed several GEO opening data classifications with TOP 100 specific methylation region values (normalized M-values on line) by machine learning, which are remarkable to classify specific anaphylaxis after monoallergen exposure. Then, we sequenced the whole-genome DNA methylation of six people (3 wormwood monoallergen atopic rhinitis patients and 3 normal-immune people) during the pollen season and analyzed the difference of the single nucleotide and DNA region. The results’ divergences were obvious (the differential single nucleotides were mostly distributed in nongene regions but the differential DNA regions of GWAS, on the other hand), which may have caused most single nucleotides to be concealed in the regions’ sequences. Therefore, we suggest that we should conduct more “pragmatic” and directly find special single-nucleotide changes after exposure to atopic allergens instead of complex correlativity. It is possible to try to use DNA methylation marks to accurately diagnose anaphylaxis and form a machine learning classification based on the single methylated CpGs.

scholarly journals ANALISIS ANGGARAN KAS SEBAGAI ALAT PERENCANAN DAN PENGENDALIAN KAS PADA PT PELABUHAN INDONESIA IV CABANG BITUNG

2018 ◽  
Vol 13 (02) ◽  
Author(s):  
Raquel Amelia Saipi ◽  
Jantje J. Tinangon ◽  
I Gede Suwetja
Keyword(s):  
Research Result ◽  
Management Tool ◽  
Budget Planning ◽  
Planning And Control ◽  
The Difference ◽  
Draw Conclusion ◽  
Internal And External Factors ◽  
The Cost ◽  
And Control ◽  
Observation Period

PT Pelabuhan Indonesia IV Branch Bitung uses the cash budget as their management tool, for the planning and control of the company's cash in achieving the success of the objectives to be achieved by the company. The purpose of this research is to know how cash budget as a planning and control tools cash in PT Pelabuhan Indonesia IV Branch Bitung with observation period of cash budget in 2015, 2016, and 2017. In this research, using descriptive method by collecting information from research result then analyze and draw conclusion from research, method of collecting information in this research by direct interview with finance section about process of preparing cash budget. The results show that the cash budgeting process in PT Pelabuhan Indonesia IV Branch Bitung uses a buttom up budgeting approach where the budget is prepared and prepared by the parties who will implement the budget. The causes of the difference (variance) between the budget and the realization of the cash budget are internal and external factors of the firm. Planning the cash budget by estimating the cost and analysis of the company's activities and for controlling its cash budget in the form of control over good deviations is favorable or unfavorable as well as revisions to deviations that occur.Keywords: Cash Budget, Planning, Controlling

scholarly journals Overfitting, Model Tuning, and Evaluation of Prediction Performance

2022 ◽  
pp. 109-139
Author(s):  
Osval Antonio Montesinos López ◽  
Abelardo Montesinos López ◽  
Jose Crossa
Keyword(s):  
Machine Learning ◽  
Predictive Modeling ◽  
Learning Model ◽  
Prediction Performance ◽  
Training Data ◽  
Statistical Machine Learning ◽  
Machine Learning Model ◽  
Data Points ◽  
The Difference ◽  
Model Tuning

AbstractThe overfitting phenomenon happens when a statistical machine learning model learns very well about the noise as well as the signal that is present in the training data. On the other hand, an underfitted phenomenon occurs when only a few predictors are included in the statistical machine learning model that represents the complete structure of the data pattern poorly. This problem also arises when the training data set is too small and thus an underfitted model does a poor job of fitting the training data and unsatisfactorily predicts new data points. This chapter describes the importance of the trade-off between prediction accuracy and model interpretability, as well as the difference between explanatory and predictive modeling: Explanatory modeling minimizes bias, whereas predictive modeling seeks to minimize the combination of bias and estimation variance. We assess the importance and different methods of cross-validation as well as the importance and strategies of tuning that are key to the successful use of some statistical machine learning methods. We explain the most important metrics for evaluating the prediction performance for continuous, binary, categorical, and count response variables.

scholarly journals Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events

2021 ◽  
Vol 12 ◽  
Author(s):  
Noam Keidar ◽  
Yonatan Elul ◽  
Assaf Schuster ◽  
Yael Yaniv
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Visual Presentation ◽  
Detection Methods ◽  
Screening Methods ◽  
Screening Programs ◽  
Machine Learning Classification ◽  
Rr Interval ◽  
Learning Classifier ◽  
The Difference

BackgroundScreening the general public for atrial fibrillation (AF) may enable early detection and timely intervention, which could potentially decrease the incidence of stroke. Existing screening methods require professional monitoring and involve high costs. AF is characterized by an irregular irregularity of the cardiac rhythm, which may be detectable using an index quantifying and visualizing this type of irregularity, motivating wide screening programs and promoting the research of AF patient subgroups and clinical impact of AF burden.MethodsWe calculated variability, normality and mean of the difference between consecutive RR interval series (denoted as modified entropy scale—MESC) to quantify irregular irregularities. Based on the variability and normality indices calculated for long 1-lead ECG records, we created a plot termed a regularogram (RGG), which provides a visual presentation of irregularly irregular rates and their burden in a given record. To inspect the potency of these indices, they were applied to train and test a machine learning classifier to identify AF episodes in gold-standard, publicly available databases (PhysioNet) that include recordings from both patients with AF and/or other rhythm disturbances, and from healthy volunteers. The classifier was trained and validated on one database and tested on three other databases.ResultsIrregular irregularities were identified using normality, variability and mean MESC indices. The RGG displayed visually distinct differences between patients with vs. without AF and between patients with different levels of AF burden. Training a simple, explainable machine learning tool integrating these three indices enabled AF detection with 99.9% accuracy, when trained on the same person, and 97.8%, when trained on patients from a different database. Comparison to other RR interval-based AF detection methods that utilize signal processing, classic machine learning and deep learning techniques, showed superiority of our suggested method.ConclusionVisualizing and quantifying irregular irregularities will be of value for both rapid visual inspection of long Holter recordings for the presence and the burden of AF, and for machine learning classification to identify AF episodes. A free online tool for calculating the indices, drawing RGGs and estimating AF burden, is available.

scholarly journals PENYUSUNAN RENCANA PELAKSANAAN PEKERJAAN BRANTAS RIVER CHECK DAM 10 (BRCD-10) KOTA BATU MALANG

2013 ◽  
Vol 7 (2) ◽  
pp. 147
Author(s):  
Gunawan Wibisono
Keyword(s):  
Heavy Rain ◽  
Project Planning ◽  
Dam Construction ◽  
Implementation Quality ◽  
Technical Specifications ◽  
The Difference ◽  
The Right ◽  
The Cost ◽  
And Control

Uncertain seasonal changes lately, causing a lot of flooding, especially in the Brantas River Basin, causing several volcanoes in the upper reaches of the Brantas River are also often carries sediment in case of floods or heavy rain, one of these volcanoes are Arjuno Mount, many of carrying materials sedimentation. One way that can be used to reduce and control the sediment, along the Brantas River by building Sabo Dam. Sabo Dam construction plan targets not only in the upper Brantas River, but also in downstream areas of the Brantas River. Sabo Dam construction by the Contractor with Grade 7 is expected to be completed in accordance with the planning purpose, because the good planning and direction will be able to save time, costs and problems (risks) that will bring the work to the activities of its main objectives, namely the right time, right cost and right quality. Implementation work methods to used for complete the development work Sabo Dam is coffering which planned uses "Phase Half-Span", which spans half a dodger and the other half worked for the evader landscape flow. The cost of implementing the budget obtained after analysis is Rp. 5,212,063,817.11 to the difference obtained Rp. 342,856,956.89 or 6,172 % from the value of the contract, the implementation quality of existing jobs on Sabo Dam work has been largely in accordance with the technical specifications have been prepared. Deviations occur, the laying material (aggregates and sand) that is not clean, while the implementation of existing K3 is in conformity with the regulations to be referenced by the Contractor in the preparation of safety plan, only for the application to use the APD is not yet implemented. Keywords: project planning, sabo dams, check dams, waterworks

scholarly journals How to Use CA-125 More Effectively in the Diagnosis of Deep Endometriosis

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Marco Aurelio Pinho Oliveira ◽  
Thiers Soares Raymundo ◽  
Leila Cristina Soares ◽  
Thiago Rodrigues Dantas Pereira ◽  
Alessandra Viviane Evangelista Demôro
Keyword(s):  
Area Under The Curve ◽  
Severe Form ◽  
Ca 125 ◽  
Time Interval ◽  
Menstrual Phase ◽  
Deep Endometriosis ◽  
The Difference ◽  
Two Phases ◽  
The Cost ◽  
And Control

Deep infiltrative endometriosis (DIE) is a severe form of the disease. The median time interval from the onset of symptoms to diagnosis of endometriosis is around 8 years. In this prospective study patients were divided into two groups: cases (34 DIE patients) and control (20 tubal ligation patients). The main objective of this study was to evaluate the performance of CA-125 measurement in the menstrual and midcycle phases of the cycle, as well as the difference in its levels between the two phases, for the early diagnosis of DIE. Area Under the Curve (AUC) of CA-125 in menstrual phase and of the difference between menstrual and midcycle phases had the best performance (both with AUC = 0.96), followed by CA-125 in the midcycle (AUC = 0.89). The ratio between menstrual and midcycle phases had the worst performance. CA-125 may be useful for the diagnosis of deep endometriosis, especially when both are collected during menstruation and in midcycle. These may help to decrease the long interval until the definitive diagnosis of DIE. Multicentric studies with larger samples should be performed to better evaluate the cost-effectiveness of measuring CA-125 in two different phases of the menstrual cycle.

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