Breast Cancer Tumor Categorization using Logistic Regression, Decision Tree and Random Forest Classification Techniques

The Static-99R has been recommended for use as a first global screen for sorting out sex-convicted persons who are in need of further risk assessment. This study investigated the Static-99R’s predictive validity based on a nonselected Norwegian sample ( n = 858) of persons released from prison after having served a sex crime sentence. After a mean observation period of 2,183 days, 3.4% ( n = 29) had recidivated to a new sex offense. A higher number of recidivists were found among those with higher Static-99R total scores. The predictive contribution from each of the ten Static-99R risk items was investigated using standard logistic regression, proportional hazard regression, and random forest classification algorithm. The overall results indicate that the Static-99R is relevant as a risk screen in a Norwegian context, providing similar results concerning predictive accuracy as previous studies.

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Effectiveness of Random Forest Classification Techniques for Feature Selection on a Large Extra Corporeal Membrane Oxygenation Dataset

American Journal of Clinical Pathology ◽

10.1093/ajcp/144.suppl2.175 ◽

2015 ◽

Vol 144 (suppl 2) ◽

pp. A175-A175

Author(s):

James Szymanski ◽

Ivo Francischetti

Keyword(s):

Feature Selection ◽

Random Forest ◽

Extra Corporeal Membrane Oxygenation ◽

Classification Techniques ◽

Random Forest Classification ◽

Membrane Oxygenation ◽

Forest Classification

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PENERAPAN ALGORITMA KLASIFIKASI RANDOM FOREST UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT DI KOPERASI MITRA SEJAHTERA

Infotech: Journal of Technology Information ◽

10.37365/it.v6i1.61 ◽

2020 ◽

Vol 6 (1) ◽

pp. 7-14

Author(s):

Achmad Udin Zailani ◽

Nugraha Listiana Hanun

Keyword(s):

Random Forest ◽

Decision Tree ◽

Decision Tree Model ◽

Tree Model ◽

Random Forest Classification ◽

Forest Classification ◽

Study Results ◽

Credit Worthiness ◽

The Impact ◽

Credit Analysis

In English : Credit is the provision of money or bills which can be equalized with an agreement or deal between the bank and another parties that requires the borrower to pay off the debt after a certain period of time through interest. Before the cooperative approves the credit proposed by the debtor, the cooperative conducts a credit analysis of borrowers whether the credit application is approved or disapproved. This study objectives to predict creditworthiness by applying the Random Forest Classification Algorithm in order to provide a solution for determining the creditworthiness.This research method is absolute experimental research that leads to the impact resulting from experiments on the application of the decision tree model of the Random Forest Classification Algorithm’s approach. The study results using the Random Forest Classification Algorithm’s are able to analyze problem credit and disproblems debtors with an accuracy value of 87.88%. Besides that,. decision tree model was able to improve the accuracy in analyzing the credit worthiness of borrowers who filed. In Indonesian : Kredit adalah penyediaan uang atau tagihan yang dapat dipersamakan atas persetujuan atau kesepakatan pinjam meminjam antara bank dengan pihak lain yang mewajibkan pihak peminjam melunasi utangnya setelah jangka waktu tertentu dengan pemberian bunga. Koperasi Mitra Sejahtera menghadapi masalah pembayaran pihak peminjam atas tunggakan kredit. Penelitian ini bertujuan untuk memprediksi kelayakan kredit dengan penerapan Algoritma Klasifikasi Random Forest agar dapat memberikan solusi untuk penentuan kelayakan pemberian kredit. Metode penelitian ini adalah riset eksperimen absolut yang mengarah kepada dampak yang dihasilkan dari eksperimen atas penerapan model pohon keputusan menggunakan pendekatan Algoritma Klasifikasi Random Forest. Hasil pengujian dengan algoritma klasifikasi Random Forest mampu menganalisis kredit yang bermasalah dan yang debitur yang tidak bermasalah dengan nilai akurasi sebesar 87,88%. Di samping itu, model pohon keputusan ternyata mampu meningkatkan akurasi dalam menganalisis kelayakan kredit yang diajukan calon debitur.

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PENERAPAN ALGORITMA KLASIFIKASI RANDOM FOREST UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT DI KOPERASI MITRA SEJAHTERA

Infotech: Journal of Technology Information ◽

10.37365/jti.v6i1.61 ◽

2020 ◽

Vol 6 (1) ◽

pp. 7-14

Author(s):

Achmad Udin Zailani ◽

Nugraha Listiana Hanun

Keyword(s):

Random Forest ◽

Decision Tree ◽

Decision Tree Model ◽

Tree Model ◽

Random Forest Classification ◽

Forest Classification ◽

Study Results ◽

Credit Worthiness ◽

The Impact ◽

Credit Analysis

Credit is the provision of money or bills which can be equalized with an agreement or deal between the bank and another parties that requires the borrower to pay off the debt after a certain period of time through interest. Before the cooperative approves the credit proposed by the debtor, the cooperative conducts a credit analysis of borrowers whether the credit application is approved or disapproved. This study objectives to predict creditworthiness by applying the Random Forest Classification Algorithm in order to provide a solution for determining the creditworthiness.This research method is absolute experimental research that leads to the impact resulting from experiments on the application of the decision tree model of the Random Forest Classification Algorithm’s approach. The study results using the Random Forest Classification Algorithm’s are able to analyze problem credit and disproblems debtors with an accuracy value of 87.88%. Besides that,. decision tree model was able to improve the accuracy in analyzing the credit worthiness of borrowers who filed.

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Texture-Based Breast Cancer Prediction in Full-Field Digital Mammograms Using the Dual-Tree Complex Wavelet Transform and Random Forest Classification

Breast Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-319-07887-8_30 ◽

2014 ◽

pp. 209-216 ◽

Cited By ~ 3

Author(s):

Emmanouil Moschidis ◽

Xin Chen ◽

Chris Taylor ◽

Sue M. Astley

Keyword(s):

Breast Cancer ◽

Wavelet Transform ◽

Random Forest ◽

Complex Wavelet Transform ◽

Full Field ◽

Random Forest Classification ◽

Cancer Prediction ◽

Forest Classification ◽

Complex Wavelet

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A Hybrid Approach for Heart Disease Prediction

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit217531 ◽

2021 ◽

pp. 95-101

Author(s):

Aamir Khan ◽

Dr. Sanjay Jain

Keyword(s):

Data Mining ◽

Logistic Regression ◽

Heart Disease ◽

Random Forest ◽

Research Work ◽

Hybrid Approach ◽

Disease Prediction ◽

Random Forest Classification ◽

Forest Classification ◽

Analysis Of Performance

The data mining (DM) is a process that deals with mining of valuable information from the rough data. The method of prediction analysis (PA) is implemented for predicting the future possibilities on the basis of current information. This research work is planned on the basis of predicting the heart disease. The coronary disorder can be forecasted in different phases in which pre-processing is done, attributes are extracted and classification is performed. The hybrid method is introduced on the basis of RF and LR.The Random Forest classification is adopted to extract the attributes and the classification process is carried out using logistic regression. The analysis of performance of introduced system is done with regard to accuracy, precision and recall. It is indicated that the introduced system will be provided accuracy approximately above 90% while predicting the heart disease.

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