Search Results - (( risk prediction model algorithm ) OR ( _ application ((bee algorithm) OR (tree algorithm)) ))

Predicting Customer Behaviour on Buying Life Insurance using Machine Learning by Fabu Ayub A Ghafar, Norhana Abd Rahim, Yumn Suhaylah Yusoff

A comparison of decision tree, logistic regression, artificial neural network and random forest algorithms to predict suicidal ideation among young adults in Malaysia by Chan, Sin Yin, Ch’ng, Chee Keong

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A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets by Mohd Razali, Muhamad Hasbullah, Saian, Rizauddin, Yap, Bee Wah, Ku-Mahamud, Ku Ruhana

“…Ant-tree-miner (ATM) has an advantage over the conventional decision tree algorithm in terms of feature selection. …”
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Classification model for hotspot occurrences using a decision tree method by Sitanggang, Imas Sukaesih, Ismail, Mohd Hasmadi

“…This work demonstrates the application of a decision tree algorithm, namely the C4.5 algorithm, to develop a classification model from forest fire data in the Rokan Hilir district, Indonesia. …”
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Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout by Dzakiyullah, Nur Rachman

“…The first experiment revealed that the Algorithm Adaptation framework outperformed Problem Transformation methods across most metrics. …”
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Early prediction of acute kidney injury using machine learning algorithms by Ismail, Amelia Ritahani, Abdul Aziz, Normaziah, Dzaharudin, Fatimah, Mat Ralib, Azrina, Md Nor, Norzaliza, Yahya, Norzariyah

“…The application of machine learning algorithms in the medical sector is gaining increased attention in the last few decades. …”
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Classification of cervical cancer using random forest by Bahirah, Mohd Bashah, Ku Muhammad Naim, Ku Khalif, Nor Azuana, Ramli

“…Model evaluation has been conducted to identify the robust data mining algorithm in the prediction of cervical cancer risk. …”
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Classification prediction of PM10 concentration using a tree-based machine learning approach by Wan Nur Shaziayani, Ul-Saufie, Ahmad Zia, Mutalib, Sofianita, Mohamad Noor, Norazian, Zainordin, Nazatul Syadia

“…Therefore, in this study, three machine learning algorithms—namely, decision tree (DT), boosted regression tree (BRT), and random forest (RF)—were applied for the prediction of PM10 in Kota Bharu, Kelantan. …”
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Improved tree routing protocol in zigbee networks by Al-Harbawi, Mostafa Kamil Abdulhusain

“…ImpTR protocol uses an approach to select next hope depending on new algorithm and uses the same tree topology construction for distributing address to all sensor nodes in the network. …”
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Prediction of COVID-19 patient using supervised machine learning algorithm by M., Buvana, K., Muthumayil

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Automated model selection for corporation credit risk assessment using machine learning / Zulkifli Halim by Halim, Zulkifli

“…Credit risk assessment is the procedure by the investors or lenders to predict the chances of loan default to measuring the risk. …”
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Prediction of Fetal Health Status Using Machine Learning by Naidile S, Saragodu, Shreedhara N, Hegde, Harprith, Kaur

“…We integrated a range of machine learning algorithms, including logistic regression, support vector machines, decision trees, and random forests, to train and test our model. …”
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Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset by Abd Rahman, M. Hafiz Fazren, Wan Salim, Wan Wardatul Amani, Abd-Wahab, Firdaus

“…However, challenges remain in obtaining an accurate model suitable for real world application. Most disease risk-prediction modelling are found to be specific to a local population. …”
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Forecasting model based on LSSVM and ABC for natural resource commodity by Yusof, Yuhanis, Kamaruddin, Siti Sakira, Husni, Husniza, Ku-Mahamud, Ku Ruhana, Mustaffa, Zuriani

“…Reliable forecast of the price of natural resource commodity is of interest for a wide range of applications.This includes generating macroeconomic projections and in assessing macroeconomic risks.Various approaches have been introduced in developing the required forecasting models.In this paper, a forecasting model based on an optimized Least Squares Support Vector Machine is proposed.The determination of hyper-parameters is performed using a nature inspired algorithm, Artificial Bee Colony.The proposed forecasting model is realized is gold price forecasting.The undertaken experiments showed that the prediction accuracy and Mean Absolute Percentage Error produced by the proposed model is better compared on the one produced using Least Squares Support Vector Machine as an individual.…”
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Streptococcus gallolyticus infection and its Interrelation with colorectal cancer: diagnostic accuracy of statistical and machine learning models for early detection algorithm by Mohammad Aidid, Edre, Hamzah, Hairul Aini, Shalihin, Mohd Shaiful Ehsan, Md Nor, Azmi, Ismail, Che Muhammad Khairul Hisyam

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Hospital readmission risk prediction of COVID-19 patients using machine learning / Loo Wei Kit by Loo , Wei Kit

“…Ultimately, a novel Slime Mold Algorithm (SMA) integrated hybrid predictive model was developed. …”
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Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data by Ting, Tin Tin, Khiew, Jie Xin, Ali Aitizaz, Lee, Kuok Tiung, Teoh, Chong Keat, Hasan Sarwar

“…This system aims to prevent the rise in cybercrimes by providing a prediction of their risk levels in cybersecurity to encourage them to be more proactive in cybersecurity.…”
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Running-Related Injury Classification For Professional Runners by Lingam, Darwineswaran Raja

“…However, little is discussed on prediction parameters to be considered when studying the type of potential injury risks that may affect a particular runner. …”
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Data Mining Analysis Of Chronic Kidney Disease (CKD) Level by Mohd Harizi, Muhammad Hafizam Afiq

“…Adding the uncertain class the best accuracy obtained was 98.5% using the SMO algorithm. A predictive classification model that determines the accuracy for three classification classes was developed accordingly using the SMO algorithm.…”
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Prediction of breast cancer diagnosis using machine learning in Malaysian women by Mokhtar, Tengku Muhammad Hanis Tengku

“…Eight ML algorithms were explored in this project. k-nearest neighbour (kNN) models had a significantly better performance compared to the other seven models. …”
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