Survey on Classification Techniques for Data Mining

Agarwal, Vivek; Thakare, Saket; Jaiswal, Akshay

doi:10.5120/ijca2015907374

Cited by 11 publications

(7 citation statements)

References 10 publications

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“…REP tree is a fast decision-tree learner that builds a decision/regression tree using information obtained from the splitting criterion, and prunes it using the reduced error method [36]. Nearest neighbor classifiers are based on learning by analogy: when given an unknown sample, a k-nearest neighbor (KNN) classifier searches the pattern space for the k training samples that are closest to the unknown sample [37]. The NB classifier assumes that each variable is unrelated to the presence of any other variable; because the independent variables are assumed, only the variances of the variables for each class need to be determined [37].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…Nearest neighbor classifiers are based on learning by analogy: when given an unknown sample, a k-nearest neighbor (KNN) classifier searches the pattern space for the k training samples that are closest to the unknown sample [37]. The NB classifier assumes that each variable is unrelated to the presence of any other variable; because the independent variables are assumed, only the variances of the variables for each class need to be determined [37]. SVM produces a model that predicts the target data value using a testing set that contains only attributes; it can classify both linear and nonlinear data [37].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…The NB classifier assumes that each variable is unrelated to the presence of any other variable; because the independent variables are assumed, only the variances of the variables for each class need to be determined [37]. SVM produces a model that predicts the target data value using a testing set that contains only attributes; it can classify both linear and nonlinear data [37].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury

Hsu

Chao

Chen

et al. 2021

JPM

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Traumatic brain injury (TBI) can lead to severe adverse clinical outcomes, including death and disability. Early detection of in-hospital mortality in high-risk populations may enable early treatment and potentially reduce mortality using machine learning. However, there is limited information on in-hospital mortality prediction models for TBI patients admitted to emergency departments. The aim of this study was to create a model that successfully predicts, from clinical measures and demographics, in-hospital mortality in a sample of TBI patients admitted to the emergency department. Of the 4881 TBI patients who were screened at the emergency department at a high-level first aid duty hospital in northern Taiwan, 3331 were assigned in triage to Level I or Level II using the Taiwan Triage and Acuity Scale from January 2008 to June 2018. The most significant predictors of in-hospital mortality in TBI patients were the scores on the Glasgow coma scale, the injury severity scale, and systolic blood pressure in the emergency department admission. This study demonstrated the effective cutoff values for clinical measures when using machine learning to predict in-hospital mortality of patients with TBI. The prediction model has the potential to further accelerate the development of innovative care-delivery protocols for high-risk patients.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Section: Machine Learning Algorithmsmentioning

confidence: 99%

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Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury

Hsu

Chao

Chen

et al. 2021

JPM

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“…Naive Bayes merupakan sebuah pengklasifikasian probabilistik sederhana yang menghitung sekumpulan probabilitas dengan menjumlahkan frekuensi dan kombinasi nilai dari dataset yang diberikan [8]. Pengklasifikasian Naive Bayes diasumsikan bahwa ada atau tidaknya ciri tertentu dari suatu kelas tidak berkaitan dengan ciri kelas lainnya [9]. Dalam teori lain dijelaskan bahwa Naive Bayes merupakan pengklasifikasian dengan metode probabilitas dan statistik yang memprediksi peluang di masa depan berdasarkan pengalaman di masa sebelumnya [10].…”

Section: Metode Penelitianunclassified

Klasifikasi Rumah Tangga Miskin di Provinsi Papua Tahun 2017 Menggunakan Metode Naive Bayes

Putri¹,

Hariyanto²,

Andini³

et al. 2021

JSMS

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Kemiskinan merupakan salah satu permasalahan kependudukan yang banyak terjadi pada negara berkembang, salah satunya Indonesia. Badan Pusat Statistik (BPS) mencatat bahwa persentase penduduk miskin di Indonesia tahun 2017 mencapai 10,12 persen. Salah satu provinsi dengan persentase penduduk miskin tertinggi di Indonesia adalah Provinsi Papua, yakni sebesar 27,76 persen. Penelitian ini bertujuan untuk melakukan klasifikasi rumah tangga berdasarkan status ekonominya di Provinsi Papua yang diperoleh dari Survei Sosial Ekonomi Nasional (Susenas) 2017 menggunakan teknik data mining. Metode analisis yang digunakan adalah metode pengklasifikasian Naive Bayes. Variabel yang digunakan antara lain jenis kelamin, pendidikan KRT, lapangan usaha KRT, jenis atap terluas, jenis dinding terluas, jenis lantai terluas, sumber air minum, sumber penerangan, dan bahan bakar untuk memasak. Berdasarkan hasil analisis, disimpulkan bahwa pengklasifikasian status ekonomi rumah tangga di Provinsi Papua memiliki tingkat akurasi sebesar 80 persen dan termasuk dalam kategori “good” dengan nilai presisi sebesar 52 persen dan spesifisitas sebesar 91 persen.

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“…Some scholars have compared the classification effects of data mining classification methods in the medical field. For example, Agarwal et al [17] compared the Bayesian, SVM, and decision tree classification results using medical data. The results show that the SVM has the highest classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform

Zhang

Yin

et al. 2018

Complexity

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The arrival of the era of big data has brought new ideas to solve problems for all walks of life. Medical clinical data is collected and stored in the medical field by utilizing the medical big data platform. Based on medical information big data, new ideas and methods for the differential diagnosis of hypo-MDS and AA are studied. The basic information, peripheral blood classification counts, peripheral blood cell morphology, bone marrow cell morphology, and other information were collected from patients diagnosed with hypo-MDS and AA diagnosed in the first diagnosis. First, statistical analysis was performed. Then, the logistic regression model, decision tree model, BP neural network model, and support vector machine (SVM) model of hypo-MDS and AA were established. The sensitivity, specificity, Youden index, positive likelihood ratio (+LR), negative likelihood ratio (−LR), area under curve (AUC), accuracy, Kappa value, positive predictive value (+PV), negative predictive value (−PV) of the four model training set and test set were compared, respectively. Finally, with the support of medical big data, using logistic regression, decision tree, BP neural network, and SVM four classification algorithms, the decision tree algorithm is optimal for the classification of hypo-MDS and AA and analyzes the characteristics of the optimal model misjudgment data.

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Survey on Classification Techniques for Data Mining

Cited by 11 publications

References 10 publications

Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury

Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury

Klasifikasi Rumah Tangga Miskin di Provinsi Papua Tahun 2017 Menggunakan Metode Naive Bayes

Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform

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