OPTIMIZATION OF NAÏVE BAYES USING BACKWARD ELIMINATION FOR HEART DISEASE DETECTION

Heart disease is the main cause of death in humans. Even though preventive measures have been taken such as regulating food (diet), lowering cholesterol, and treating weight, diabetes, and hypertension, heart disease remains a major health problem. There are several factors that cause heart disease, including age, type of chest pain, high blood pressure, sugar levels, ECG test values, maximum heart rate, and induced angina. To reduce the percentage of deaths due to heart disease, we need a system that can predict heart disease. The algorithm used in this research is a combination of the Backward Elimination and Naive Bayes algorithms to increase accuracy in diagnosing heart disease. According to the results of this research, the Naive Bayes algorithm has an accuracy value of 78.90% and an Area Under Curve (AUC) value of 0.86, which is included in the good classification category. Combining the Backward Elimination and Naïve Bayes algorithms has an accuracy value of 82.31% and an Area Under Curve (AUC) value of 0.88

Prediksi Keputusan Klien Telemarketing untuk Deposito pada Bank Menggunakan Algoritma Naive Bayes Berbasis Backward Elimination

Telemarketing merupakan salah satu promosi yang dianggap paling efektif dalam mempromosikan produk, strategi pemasaran ini dilakukan oleh bank-bank untuk menawarkan produk pada klien, salah satu produk yang ditawarkan bank yaitu deposito berjangka. Sulitnya mengetahui keputusan klien Telemarketing untuk melakukan deposito berjangka pada bank, menyebabkan bank selalu menghadapi ancaman krisis keuangan. Oleh karena itu, Telemarketing bank harus dapat membuat target klien, klien mana yang berpotensi untuk melakukan deposito dengan melihat data-data klien yang ada. Dalam penelitian ini akan digunakan algoritma Naive Bayes untuk memprediksi keputusan klien Telemarketing dengan menggunakan dataset gudang data UCI Repository. Hasil pengujian menunjukkan bahwa nilai akurasi Naive Bayes sebesar 89,08%, setelah dilakukan pemilihan fitur dengan menggunakan Backward Elimination didapatkan hasil akurasi yang lebih tinggi yaitu sebesar 90,69%, dengan melihat nilai akurasi maka algoritma Naive Bayes berbasis Backward Elimination meningkatkan akurasi untuk memprediksi keputusan klien Telemarketing

Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance

In this study, the best combination of short-term heart rate variability (HRV) measures was investigated to distinguish 29 patients with congestive heart failure from 54 healthy subjects in the control group. In the analysis performed, wavelet packet transform based frequency-domain measures and several non-linear parameters were used in addition to standard HRV measures. The backward elimination and unpaired statistical analysis methods were used to select the best one among all possible combinations of these measures. Five distinct typical classifiers with different parameters were evaluated in discriminating these two groups using the leave-one-out cross validation method. Each algorithm was tested 30 times to determine the repeatability of the results. The results imply that the backward elimination method gives better performance when compared to the statistical significance method in the feature selection stage. The best performance (82.75%, 96.29%, and 91.56% for the sensitivity, specificity, and accuracy) was obtained by using the SVM classifier with 27 selected features including non-linear and wavelet-based measures. © 2013 Elsevier Ltd