A Software Tool for Determination of Breast Cancer Treatment Methods Using Data Mining Approach

Çakir, Abdülkadir; Demirel, Burçin Çakan

doi:10.1007/s10916-009-9427-x

Cited by 22 publications

(7 citation statements)

References 15 publications

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“…We will focus on applying similar methods in different populations using more data. When the amount of data increases, the results will be more robust [17]. Our approach can be extended to larger databases that store more variables and risk factors related to diabetes [22].…”

Section: Discussionmentioning

confidence: 99%

“…There is considerable interest in determining how different classification techniques from machine learning can be utilized as disease prediction tools [10–19]. These tools have been used to diagnose diabetes [3, 8–10, 20–22], meningitis [6], glaucoma [11], asthma [12], coronary artery disease [13], cancer [14–17, 23], tuberculosis [18, 24], hypertension [25], and heart arrhythmia [26].…”

Section: Introductionmentioning

confidence: 99%

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Accurate and rapid screening model for potential diabetes mellitus

Pei

Gong

Kang

et al. 2019

BMC Med Inform Decis Mak

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BackgroundPrediction or early diagnosis of diabetes is crucial for populations with high risk of diabetes.MethodsIn this study, we assessed the ability of five popular classifiers (J48, AdaboostM1, SMO, Bayes Net, and Naïve Bayes) to identify individuals with diabetes based on nine non-invasive and easily obtained clinical features, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work stress, and salty food preference. A total of 4205 data entries were obtained from annual physical examination reports for adults in the Shengjing Hospital of China Medical University during January–April 2017. Weka data mining software was used to identify the best algorithm for diabetes classification.ResultsThe results indicate that decision tree classifier J48 has the best performance (accuracy = 0.9503, precision = 0.950, recall = 0.950, F-measure = 0.948, and AUC = 0.964). The decision tree structure shows that age is the most significant feature, followed by family history of diabetes, work stress, BMI, salty food preference, physical activity, hypertension, gender, and history of cardiovascular disease or stroke.ConclusionsOur study shows that decision tree analyses can be applied to screen individuals for early diabetes risk without the need for invasive tests. This procedure will be particularly useful in developing regions with high epidemiological risk and poor socioeconomic status, and enable clinical practitioners to rapidly screen patients for increased risk of diabetes. The key features in the tree structure could further facilitate diabetes prevention through targeted community interventions, which can potentially improve early diabetes diagnosis and reduce burdens on the healthcare system.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate and rapid screening model for potential diabetes mellitus

Pei

Gong

Kang

et al. 2019

BMC Med Inform Decis Mak

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“…2016 ; Gilbert etal. 2016 ; Cakır and Demirel 2011 ; Sahle 2016 ), this is the first time that machine learning techniques have been applied to understand the importance of clinical parameters in determining within-host HIV-1 subtype B.…”

Section: Discussionmentioning

confidence: 99%

Impact of Clinical Parameters in the Intrahost Evolution of HIV-1 Subtype B in Pediatric Patients: A Machine Learning Approach

Sánchez

Cobos

Navaro

et al. 2017

Genome Biology and Evolution

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Determining the factors modulating the genetic diversity of HIV-1 populations is essential to understand viral evolution. This study analyzes the relative importance of clinical factors in the intrahost HIV-1 subtype B (HIV-1B) evolution and in the fixation of drug resistance mutations (DRM) during longitudinal pediatric HIV-1 infection. We recovered 162 partial HIV-1B pol sequences (from 3 to 24 per patient) from 24 perinatally infected patients from the Madrid Cohort of HIV-1 infected children and adolescents in a time interval ranging from 2.2 to 20.3 years. We applied machine learning classification methods to analyze the relative importance of 28 clinical/epidemiological/virological factors in the HIV-1B evolution to predict HIV-1B genetic diversity (d), nonsynonymous and synonymous mutations (dN, dS) and DRM presence. Most of the 24 HIV-1B infected pediatric patients were Spanish (91.7%), diagnosed before 2000 (83.3%), and all were antiretroviral therapy experienced. They had from 0.3 to 18.8 years of HIV-1 exposure at sampling time. Most sequences presented DRM. The best-predictor variables for HIV-1B evolutionary parameters were the age of HIV-1 diagnosis for d, the age at first antiretroviral treatment for dN and the year of HIV-1 diagnosis for ds. The year of infection (birth year) and year of sampling seemed to be relevant for fixation of both DRM at large and, considering drug families, to protease inhibitors (PI). This study identifies, for the first time using machine learning, the factors affecting more HIV-1B pol evolution and those affecting DRM fixation in HIV-1B infected pediatric patients.

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“…The Neural Network method is a neural network based on multilayer perceptron which focuses on building intelligent codes that mimic the learning mechanism of a human brain by constituting a parallel-connected network model [15]. It consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one.…”

Section: Data Mining Methodsmentioning

confidence: 99%

Prediction of mortality rates in heart failure patients with data mining methods

Boháčik¹,

Kambhampati²,

Davis³

et al. 2013

Annales UMCS, Informatica

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Heart failure is one of the severe diseases which menace the human health and affect millions of people. Half of all patients diagnosed with heart failure die within four years. For the purpose of avoiding life-threatening situations and minimizing the costs, it is important to predict mortality rates of heart failure patients. As part of a HEIF-5 project, a data mining study was conducted aiming specifically at extracting new knowledge from a group of patients suffering from heart failure and using it for prediction of mortality rates. The methodology of knowledge discovery in databases is analyzed within the framework of home telemonitoring. Several data mining methods such as a Bayesian network method, a decision tree method, a neural network method and a nearest neighbour method are employed. The accuracy for the data mining methods from the point of view of avoiding life-threatening situations and minimizing the costs is discussed. It seems that the decision tree method achieves the best accuracy results and is also interpretable for the clinicians.

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A Software Tool for Determination of Breast Cancer Treatment Methods Using Data Mining Approach

Cited by 22 publications

References 15 publications

Accurate and rapid screening model for potential diabetes mellitus

Accurate and rapid screening model for potential diabetes mellitus

Impact of Clinical Parameters in the Intrahost Evolution of HIV-1 Subtype B in Pediatric Patients: A Machine Learning Approach

Prediction of mortality rates in heart failure patients with data mining methods

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