A Decision Support System for Heart Failure Risk Prediction Based on Weighted Naive Bayes

2021 ◽  
pp. 445-460
Author(s):  
Kehui Song ◽  
Shenglong Yu ◽  
Haiwei Zhang ◽  
Ying Zhang ◽  
Xiangrui Cai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Decision Support ◽  
Decision Support System ◽  
Risk Prediction ◽  
Support System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Failure Risk

An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

2017 ◽  
Vol 68 ◽  
pp. 163-172 ◽  
Author(s):  
Oluwarotimi Williams Samuel ◽  
Grace Mojisola Asogbon ◽  
Arun Kumar Sangaiah ◽  
Peng Fang ◽  
Guanglin Li
Keyword(s):  
Heart Failure ◽  
Decision Support ◽  
Decision Support System ◽  
Risk Prediction ◽  
Support System ◽  
Failure Risk

scholarly journals A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ2 Statistical Model and Gaussian Naive Bayes

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Liaqat Ali ◽  
Shafqat Ullah Khan ◽  
Noorbakhsh Amiri Golilarz ◽  
Imrana Yakubu ◽  
Iqbal Qasim ◽  
...  
Keyword(s):  
Heart Failure ◽  
Decision Support ◽  
Decision Support System ◽  
Statistical Model ◽  
Decision Support Systems ◽  
Support System ◽  
Prediction Accuracy ◽  
Naive Bayes ◽  
Support Systems ◽  
Naïve Bayes

Heart failure (HF) is considered a deadliest disease worldwide. Therefore, different intelligent medical decision support systems have been widely proposed for detection of HF in literature. However, low rate of accuracies achieved on the HF data is a major problem in these decision support systems. To improve the prediction accuracy, we have developed a feature-driven decision support system consisting of two main stages. In the first stage, χ2 statistical model is used to rank the commonly used 13 HF features. Based on the χ2 test score, an optimal subset of features is searched using forward best-first search strategy. In the second stage, Gaussian Naive Bayes (GNB) classifier is used as a predictive model. The performance of the newly proposed method (χ2-GNB) is evaluated by using an online heart disease database of 297 subjects. Experimental results show that our proposed method could achieve a prediction accuracy of 93.33%. The developed method (i.e., χ2-GNB) improves the HF prediction performance of GNB model by 3.33%. Moreover, the newly proposed method also shows better performance than the available methods in literature that achieved accuracies in the range of 57.85–92.22%.

IMPLEMENTASI TEOREMA NAÏVE BAYES PADA SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN REKOMENDASI TEMPAT WISATA TERBAIK DISULAWESI SELATAN

2019 ◽  
Vol 2 (1) ◽  
pp. 40-46
Author(s):  
Rikardo Chandra ◽  
Izmy alwiah Musdar ◽  
Junaedy .
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Programming Language ◽  
Support System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Based ◽  
Tourist Attractions ◽  
South Sulawesi

This study aims to design and build web-based decision support system applications used to recommend the best tourist attractions in South Sulawesi to tourists. The expected benefit of this research is to help the user get the best tourist recommendation information available in South Sulawesi based on the conditions in input factors. The theorem or method used in this study, namely the theorem Naïve Bayes. The design of the system isimplemented using PHP programming language and MYSQL database. Based on the results of the research, the authors have successfully built the application of decision support system to determine the recommendation of tourist attractions in South Sulawesi with 65% accuracy based on 20 tests conducted.

scholarly journals Social Assistance Decision Support System Using the Naive Bayes Method

2021 ◽  
Vol 1 (1) ◽  
pp. 85
Author(s):  
Moh. Syaiful Anam
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Social Assistance ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Naive Bayes Method

Covid-19 telah menjadi pandemi yang menyebar hampir ke seluruh penjuru dunia. Karena proses penularannya yang begitu cepat Dalam masa pandemi covid -19, pandemi ini menyebar ke seluruh sendi kehidupan dan salah satu yang paling menjadi perhatian adalah dibidang sosial ekonomi. Banyak terdapat bantuan Sosial (Bansos) yang disalurkan baik oleh pemerintah ataupun pihak swasta lain. Penelitian ini bertujuan untuk membuat sistem pendukung keputusan bantuan sosial menggunakan metode Naive Bayes, selanjutnya melakukan Analisa menggunakan tabel Confusion Matrix.  Dalam menyelesaikan masalah dengan menggunakan metode Naive Bayes dari hasil pembahasan yang dilakukan dapat ditarik kesimpulan Naive Bayes dan aturan yang dihasilkan memiliki tingkat akurasi tinggi (good) yaitu sebesar 73% dan Sementara nilai Precision sebesar 92% dan Recall sebesar 86%. Sehingga metode Naive Bayes dapat diterapkan dalam menentukan prediksi yang lebih banyak dan potensial aturan yang dihasilkan untuk membantu menentukan pemberian bantuan sosial.

scholarly journals Penentuan Lokasi Fasilitas Kesehatan Strategis Menggunakan Metode Naive Bayes pada RSU Bintang

2021 ◽  
Vol 6 (2) ◽  
pp. 52
Author(s):  
Aniek Suryanti Kusuma ◽  
Welda Welda ◽  
I Komang Juliana
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Health Facility ◽  
Support System ◽  
Naive Bayes ◽  
Current System ◽  
Naïve Bayes ◽  
Health Facilities ◽  
The Right

At present the selection of strategic health facility locations is not easy, to determine the right location and in accordance with the needs of patients must use the right calculation. Bintang General Hospital (RSU Bintang) has difficulties in determining the strategic location of new health facilities. The difficulty is due to the absence of data processing from the current system so that in determining the location of strategic health facilities is not based on data that has been analyzed. Based on the problems experienced by RSU Bintang and to assist in making a decision in establishing a strategic health facility location, a study was made to design a decision support system that can perform calculations to determine the location of the most strategic health facility with the title "Decision Support System. Determining the Location of Strategic Health Facilities Using the Naive Bayes Method at RSU Bintang”. Decision support system that is built will have several functions, such as processing patient register data, user data processing, alternative location data processing, criteria data processing, data processing rules, Naive Bayes calculations and managing several reports that can be used as decision support for the RSU Bintang. in determining the location of the most strategic health facilities. In this system, testing has been done by using blackbox testing which gets the test results in accordance with the system design.

Decision Support System for Admission Selection and Positioning Human Resources by Using Naive Bayes Method

2017 ◽  
Vol 23 (3) ◽  
pp. 2495-2497 ◽  
Author(s):  
Dyna Marisa Khairina ◽  
Septya Maharani ◽  
. Ramadiani ◽  
Heliza Rahmania Hatta
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Human Resources ◽  
Support System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Naive Bayes Method
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