scholarly journals Análisis y comparación de modelos de clasificación de aprendizaje automático aplicado a riesgo crediticio

2018 ◽  
pp. 122-127
Keyword(s):  
Machine Learning ◽  
Current Knowledge ◽  
Gradient Boosting ◽  
Classification Models ◽  
Process Automation ◽  
Financial Industry ◽  
Machine Learning Classification ◽  
Nonperforming Loans ◽  
Mathematical Algorithms ◽  
Credit Granting

Análisis y comparación de modelos de clasificación de aprendizaje automático aplicado a riesgo crediticio Analysis and comparison of machine learning classification models applied to credit approval Jorge Brian Alarcón Flores, Jiam Carlos López Malca, Luis Ruiz Saldarriaga, Christian Walter Sarmiento Román Maestría en Informática con mención en Ciencias de la Computación, Pontificia Universidad Católica del Perú. Lima, Perú. Recibido el 18 de noviembre del 2017, aceptado el 26 de noviembre del 2017 DOI: https://doi.org/10.33017/RevECIPeru2017.0014/ Resumen El sector industrial financiero se ha convertido en un sector muy competitivo a nivel mundial. Dentro de este contexto, la decisión del otorgamiento de crédito es uno de los procesos más importantes del cual dependen indicadores críticos del negocio como son las colocaciones, las recuperaciones y el índice de morosidad.  Este proceso se ha basado históricamente en expertos del negocio, quienes en base a su experiencia determinaban en función a ciertas variables de comportamiento del solicitante, si debían otorgar o no el crédito.  En esta última década, el desarrollo de tecnologías como la inteligencia artificial y el aprendizaje de máquina han aportado mucho en la automatización de este proceso.  El presente trabajo tiene como objetivo principal el análisis de varios algoritmos matemáticos basados en el aprendizaje de máquina en las predicciones de otorgamiento de crédito, dando una explicación objetiva de los resultados y sugiriendo las siguientes investigaciones que se desarrollarán con el fin de obtener mejores resultados en los algoritmos matemáticos existentes. Como resultados de la experimentación de determinó que el mejor modelo fue el de Gradient Boosting, con una exactitud de 83.71%.  Descriptores:  Abstract The financial industry has become into a very competitive sector worldwide.  In that sense, the credit granting decision is one of the most important process of all, and in whose accuracy, rests the good performance of several critical business KPI's such as loans level, credit recoveries level and nonperforming loans ratios.  This key process has historically based on the experts’ judgement, and have taken the decision of granting or not credit loans according to several customer credit behavior elements.  In the last decade, the developing of certain technology such AI and machine learning has allowed this process automation.  The present paper has its main goal, the analysis of several mathematical algorithms based on machine learning and the exposition of which of them have the better results in credit granting predictions to collaborate with current knowledge in this particular issue, giving an objective explanation of the results and suggesting following researches to be developed in order to get better results in existing mathematical algorithms. As results of the experimentation determined that the best model was Gradient Boosting, with an accuracy of 83.71%. Keywords: artificial intelligence, machine learning, credit risk, mathematic models, gradient boosting.

scholarly journals Automated invasive cervical cancer disease detection at early stage through suitable machine learning model

2021 ◽  
Vol 3 (10) ◽  
Author(s):  
Sohely Jahan ◽  
M. D. Saimun Islam ◽  
Linta Islam ◽  
Tamanna Yesmin Rashme ◽  
Ayesha Aziz Prova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Random Forest ◽  
Invasive Cervical Cancer ◽  
Research Work ◽  
Gradient Boosting ◽  
Classification Models ◽  
Cancer Disease ◽  
Suggested Approach ◽  
Machine Learning Classification

AbstractCervical cancer is a common cancer that affects women all over the world. This is the fourth leading cause of death among women and has no symptoms in its early stages. At the cervix, cervical cancer cells develop slowly. If it can be detected early, this cancer can be successfully treated. Health professionals are now facing a major challenge in detecting such cancer until it spreads rapidly. This study applied various machine learning classification methods to predict cervical cancer using risk factors. The main aim of this research work is to be described of the performance variation of eight most classifications algorithm to detect cervical cancer disease based on the selection of various top features sets from the dataset. Multilayer Perceptron (MLP), Random Forest and k-Nearest Neighbor, Decision Tree, Logistic Regression, SVC, Gradient Boosting, AdaBoost are examples of machine learning classification algorithms that have been used to predict cervical cancer and help in early diagnosis. A variety of approaches are used to avoid missing values in the dataset. To choose the various best features, a combination of feature selection techniques such as Chi-square, SelectBest and Random Forest was used. The performance of those classifications is evaluated using the accuracy, recall, precision and f1-score parameters. On a variety of top feature sets, MLP outperformed other classification models. The majority of classification models, on the other hand, claim to have the highest accuracy on the top 25 features in dataset splitting ratio (70:30). For each model, the percentage of correctly classified instances has been presented and all of the results are then discussed. Medical professionals will be able to use the suggested approach to perform research on cervical cancer.

scholarly journals Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Annachiara Tinivella ◽  
Luca Pinzi ◽  
Giulio Rastelli
Keyword(s):  
Machine Learning ◽  
Carbonic Anhydrase ◽  
A Priori ◽  
Selective Inhibition ◽  
Great Promise ◽  
Classification Models ◽  
Machine Learning Classification ◽  
Central Interest ◽  
Human Carbonic Anhydrase ◽  
In Silico Models

AbstractThe development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms.

Prediction of hepatocellular carcinoma patient survival using machine learning classification rules.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15649-e15649
Author(s):  
Wei Zhou ◽  
Huan Chen ◽  
Wenbo Han ◽  
Ji He ◽  
Henghui Zhang
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Gradient Boosting ◽  
Great Promise ◽  
Survival Prediction ◽  
Surgical Removal ◽  
Features Selection ◽  
Machine Learning Classification ◽  
Learning Techniques ◽  
Pathologic Features

e15649 Background: The outcome prediction of hepatocellular carcinoma (HCC) is conventionally determined by evaluating tissue samples obtained during surgical removal of the primary tumor focusing on their clinical and pathologic features. Recently, accumulating evidence suggests that cancer development is comprehensively modulated by the host’s immune system underlying the importance of immunological biomarkers for the prediction of HCC prognosis. However, an integrated predictive algorism incorporating clinical characteristic and immune features still remain to be established. Methods: We obtained respectable stage II HCC specimens, along with adjacent para-tumor tissues from 221 patients who underwent surgical resection at Eastern Hepatobiliary Surgery Hospital, (Shanghai, China) from 2015 through April 2018. Characteristics such as CD8+, CD163+, tumor-infiltrating lymphocytes (TILs) were obtained for further model construction used to predict the status of 3 survival indexes: Overall Survival (OS ,≤ 24 or > 24 month), Progression Free Survival (PFS, ≤ 6 or > 6 month), and Recurrence/Death (RD). Mutual information and coefficient between each feature and the survival indexes were tested to remove low scoring features after data cleaning and standardization. Furthermore, recursive features selection was preformed to obtain the optimal features combination. Finally, supervised learning techniques include either boosting or bagging strategy were used to fit and predict model with a grid-search method optimizing the parameters. Meanwhile, a cross validation procedure with 0.2 proportion of test cohort was randomly carried out for 10 times to evaluate the model. Results: We finally confirmed 15 biomarkers from the 46 candidates as features for the survival status prediction by using a 221 patients cohort. Among them, the top 10 most important biomarkers, included both clinical and immune attributes. The AUC of our model for survival indexes (OS, PFS, RD) was ranged from 0.76 (RD) to 0.8 (PFS), and the accuracy was above 0.85. Conclusions: We describe the integrative analysis of the clinical and immune features which collectively contribute to the survival index of HCC. Machine learning techniques, such as Gradient Boosting and random forest classifier , have a great promise for using in HCC cancer survival prediction.

scholarly journals Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models

2021 ◽  
Vol 10 (10) ◽  
pp. 680
Author(s):  
Annan Yang ◽  
Chunmei Wang ◽  
Guowei Pang ◽  
Yongqing Long ◽  
Lei Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Complex Terrain ◽  
Large Scale ◽  
Area Under The Curve ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Weight Of Evidence ◽  
Gradient Boosting ◽  
Machine Learning Classification ◽  
Extreme Gradient Boosting

Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.

scholarly journals Count Vectorized Spam and Ham Discernment of Short Message Service using Machine Learning Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 557-561
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Gradient Boosting ◽  
Svm Classifier ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Classification ◽  
Tree Classifier

With the growing volume and the amount of spam message, the demand for identifying the effective method for spam detection is in claim. The growth of mobile phone and Smartphone has led to the drastic increase in the SMS spam messages. The advancement and the clean process of mobile message servicing channel have attracted the hackers to perform their hacking through SMS messages. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the owners. With this background, this paper focuses on predicting the Spam SMS messages. The SMS Spam Message Detection dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of Spam message detection is achieved in four ways. Firstly, the distribution of the target variable Spam Type the dataset is identified and represented by the graphical notations. Secondly, the top word features for the Spam and Ham messages in the SMS messages is extracted using Count Vectorizer and it is displayed using spam and Ham word cloud. Thirdly, the extracted Counter vectorized feature importance SMS Spam Message detection dataset is fitted to various classifiers like KNN classifier, Random Forest classifier, Linear SVM classifier, Ada Boost classifier, Kernel SVM classifier, Logistic Regression classifier, Gaussian Naive Bayes classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Multinomial Naive Bayes classifier. Performance analysis is done by analyzing the performance metrics like Accuracy, FScore, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator. Experimental Results shows that the Multinomial Naive Bayes classifier have achieved the effective prediction with the precision of 0.98, recall of 0.98, FScore of 0.98 , and Accuracy of 98.20%..

scholarly journals Supervised machine learning based liver disease prediction approach with LASSO feature selection

2021 ◽  
Vol 10 (6) ◽  
pp. 3369-3376
Author(s):  
Saima Afrin ◽  
F. M. Javed Mehedi Shamrat ◽  
Tafsirul Islam Nibir ◽  
Mst. Fahmida Muntasim ◽  
Md. Shakil Moharram ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Liver Disease ◽  
Decision Tree ◽  
Medical Science ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Prediction Approach

In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system. 

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