Predictive data mining approaches in medical diagnosis: A review of some diseases prediction

Ghorbani, Ramin; Ghousi, Rouzbeh

doi:10.5267/j.ijdns.2019.1.003

Cited by 54 publications

(39 citation statements)

References 28 publications

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“…[3] [4] Data Mining adalah teknik yang dilakukan pada basis data besar untuk mengekstraksi pola tersembunyi dengan menggunakan strategi kombinasional dari analisis statistik, pembelajaran mesin, dan teknologi basis data. Data mining medis adalah bidang penelitian yang sangat penting karena pentingnya dalam pengembangan berbagai aplikasi dalam domain perawatan kesehatan yang berkembang [5]. Data mining dikarakterisasi sebagai pencarian informasi yang berguna melalui kumpulan data yang sangat besar.…”

Section: Pendahuluanunclassified

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Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining

Derisma¹

2020

JAIC Polibatam

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Heart disease is a disease that contributes to a relatively high mortality rate. The rate of human death caused by disease in the heart is a widespread problem in the world. The main objective of this study is to predict people with heart disease using the publicly available dataset in the UCI Repository with the Heart Disease dataset. To obtain the best classification algorithm is by comparing three Algoritma Naive Bayes, Random Forest, Neural Network algorithms, which are frequently used to predict people with heart disease. Comparison results show that Naive Bayes ' algorithm is a precise and accurate algorithm used to predict people with heart disease with a percentage of 83 %.

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Section: Pendahuluanunclassified

“…Hasil penelitian diperoleh nilai akurasi tertinggi sebesar 80.38% pada algoritma random forest. Penelitian telah dilakukan Abdul Rohman[9] menggunakan algoritma neural network, neigboard knearest dan data pasien menggunakan C4 5. untuk akurasi prediksi penyakit penyakit jantung jantung dapat diamati bahwa metode terbaik adalah jaringan saraf untuk nilai akurarasi 86, 06 %.…”

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Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining

Derisma¹

2020

JAIC Polibatam

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“…Recent advancement in several fields has led to a large amount of collected data [1]. Since analyzing the considerable amount of data to reach useful information is a tedious task for humankind, data mining techniques can be used to discover valuable and significant knowledge from the data [2]. It is well-known that universities are operating in a very complex and highly competitive environment [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Comparing Different Resampling Methods in Predicting Students’ Performance Using Machine Learning Techniques

2020

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In today's world, due to the advancement of technology, predicting the students' performance is among the most beneficial and essential research topics. Data Mining is extremely helpful in the field of education, especially for analyzing students' performance. It is a fact that predicting the students' performance has become a severe challenge because of the imbalanced datasets in this field, and there is not any comparison among different resampling methods. This paper attempts to compare various resampling techniques such as Borderline SMOTE, Random Over Sampler, SMOTE, SMOTE-ENN, SVM-SMOTE, and SMOTE-Tomek to handle the imbalanced data problem while predicting students' performance using two different datasets. Moreover, the difference between multiclass and binary classification, and structures of the features are examined. To be able to check the performance of the resampling methods better in solving the imbalanced problem, this paper uses various machine learning classifiers including Random Forest, K-Nearest-Neighbor, Artificial Neural Network, XG-boost, Support Vector Machine (Radial Basis Function), Decision Tree, Logistic Regression, and Naïve Bayes. Furthermore, the Random hold-out and Shuffle 5-fold cross-validation methods are used as model validation techniques. The achieved results using different evaluation metrics indicate that fewer numbers of classes and nominal features will lead models to better performance. Also, classifiers do not perform well with imbalanced data, so solving this problem is necessary. The performance of classifiers is improved using balanced datasets. Additionally, the results of the Friedman test, which is a statistical significance test, confirm that the SVM-SMOTE is more efficient than the other resampling methods. Moreover, The Random Forest classifier has achieved the best result among all other models while using SVM-SMOTE as a resampling method. INDEX TERMS Classification, data mining, educational data mining, imbalanced data problem, machine learning, resampling methods, statistical analysis.

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“…Determining the wrong treatment for patients not only waste time and money but also can cause unfavorable consequences such as a patient's death. Accordingly, it is necessary to have a system for diagnosing and choosing the proper treatment [2]. Machine learning is the experimental study of analytical models and algorithms that builds a mathematical model of sample data to present predictions or decisions [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…These studies proved that using ensemble models can improve prediction results. Moreover, Ghorbani and Ghousi [2] reviewed the predictive data mining approaches in medical diagnosis, and the results declared that researchers had obtained better prediction accuracy while using ensemble models. The lack of using hybrid and ensemble models in predicting mortality risk within the ICU patients is evident, but using these models is not the only vital factor on the subject of improving prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset

et al. 2020

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Due to the development of biomedical equipment and healthcare level, especially in the Intensive Care Unit (ICU), a considerable amount of data has been collected for analysis. Mortality prediction in the ICUs is considered as one of the most important topics in the healthcare data analysis section. A precise prediction of the mortality risk for patients in ICU could provide us with valuable information about patients' lives and reduce costs at the earliest possible stage. This paper aims to introduce a new hybrid predictive model using the Genetic Algorithm as a feature selection method and a new ensemble classifier based on the combination of Stacking and Boosting ensemble methods to create an early mortality prediction model on a highly imbalanced dataset. The SVM-SMOTE method is used to solve the imbalanced data problem. This paper compares the new model with various machine learning models to validate the efficiency of the introduced model. The achieved results using the shuffle 5-fold cross-validation and random hold-out methods indicate that the new hybrid model has the best performance among other classifiers. Additionally, the Friedman test is applied as a statistical significance test to examine the differences between classifiers. The results of the statistical analysis prove that the proposed model is more effective than other classifiers. Furthermore, the proposed model is compared to APACHE and SAPS scoring systems and is benchmarked against state-of-the-art predictive models applied to the MIMIC dataset for experimental validation and achieved promising results as it outperformed the state-of-the-art models.

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Predictive data mining approaches in medical diagnosis: A review of some diseases prediction

Cited by 54 publications

References 28 publications

Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining

Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining

Comparing Different Resampling Methods in Predicting Students’ Performance Using Machine Learning Techniques

A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset

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