Data mining techniques with machine learning algorithm to predict patients of heart disease

Mulyawan,; Bahtiar, Agus; Dwilestari, Githera; Basysyar, Fadhil Muhammad; Suarna, Nana

doi:10.1088/1757-899x/1088/1/012035

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…The K-Nearest Neighbor algorithm utilized a value of K = 5 with Mixed Measures of the Mixed Euclidean distance for classification [18]. The Support Vector Machine algorithm was utilized by kernel cache = 200 and convergence epsilon = 0.001 [19]. The Naïve Bayes algorithm used a distribution model.…”

Section: Resultsmentioning

confidence: 99%

Comparative Analysis of Restock Needs Bottled Water Using K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and the Naïve Bayes Algorithm

Pratiwi,

Sumarlinda,

Nastiti

2023

IJIST-DAS

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Restocking goods is essential for bottled drinking water to ensure smooth production and maintain a stable product supply. This research aims to compare the K-Nearest Neighbor, Support Vector Machine, and the Naïve Bayes algorithm to predict the need to restock bottled water. The data set for training and training data is taken from Adimaru's Agent. The comparative analysis with three algorithms gives the results of the prediction analysis for the accuracy value of K-NN is 88.20%, SVM is 84.51%, and Naïve Bayes is 66.20%. The AUC values of the three result algorithms include Good Classification. The comparison of the K-NN and SVM with T-Test algorithms get obtained the best performance with an alpha value is 0.102. From this accuracy value, the classification method of the K-Nearest Neighbor algorithm has the best predictive model results for restocking needs of bottled water goods.

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Section: Resultsmentioning

confidence: 99%

Comparative Analysis of Restock Needs Bottled Water Using K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and the Naïve Bayes Algorithm

Pratiwi,

Sumarlinda,

Nastiti

2023

IJIST-DAS

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“…sedangkan prediksi adalah proses menemukan pola dari suatu data dengan variabel lain yang akan datang. contoh teknik prediksi adalah klasifikasi [9].…”

Section: Tinjauan Pustaka 2data Miningunclassified

Implementasi Teknik Data Mining Pada Data Kunjungan Wisatawan Menggunakan K-Means Clustering Di Hotel Bintang Kota Cirebon

Wulandari,

Astuti,

M. Basysyar

2024

jati

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Kota Cirebon kini juga disebut sebagai “kota wisata” dan memiliki berbagai tempat wisata yang menarik. Hotel merupakan jenis fasilitas akomodasi yang menyediakan berbagai layanan seperti tempat menginap, makanan, dan layanan lainnya untuk sementara kepada masyarakat umum dan dijalankan secara komersial. Dinas Kebudayaan dan Pariwisata seringkali mengalami permasalahan karena belum melakukan pemetaan kunjungan tamu hotel sehingga berdampak pada pengelolaan hotel bintang di kota cirebon. Untuk mengatasi masalah ini maka digunakan data kunjungan wisatawan dari januari hingga desember tahun 2021 bersumber dari Dinas Kebudayaan dan Pariwisata Kota Cirebon menggunakan teknik penerapan data mining untuk mengelola informasi data kunjungan wisatawan di hotel bintang di Kota Cirebon menggunakan K-Means Clustering. Kemudian, Validasi hasil klasterisasi dapat dilakukan menggunakan Davies Bouldin Index (DBI). Berdasarkan hasil pengelompokan pada RapidMiner menggunakan algoritma k-means, data dikelompokan menjadi 7 kelompok. Kelompok 3 merupakan tingkat dengan kunjungan wisatawan paling rendah, kelompok 1 merupakan tingkat dengan kunjungan wisatawan rendah 1, kelompok 6 tingkat dengan kunjungan wisatawan rendah 2, kelompok 0 merupakan tingkat dengan kunjungan wisatawan sedang, kelompok 4 merupakan tingkat dengan kunjungan wisatawan tinggi 1, kelompok 2 merupakan tingkat dengan kunjungan wisatawan tinggi 2 dan kelompok 5 merupakan tingkat dengan kunjungan wisatawan sangat tinggi. Oleh karena itu, dilakukannya penelitian mengenai penerapan data mining dengan jumlah wisatawan terbanyak yang ramai berkunjung dapat memberikan manfaat yang besar bagi manajemen hotel dan Dinas Kebudayaan dan Pariwisata Kota Cirebon dalam meningkatkan fasilitas pelayanan yang lebih baik

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“…The system is designed to have 9 rules and achieves an accuracy of 93.88%. Bahtiar et al [8] presented a hybrid model based on the majority vote. This model uses three types of artificial neural networks: two multilayer perceptron neural networks and one radial base function network.…”

Section: Related Workmentioning

confidence: 99%

Algorithm of Predicting Heart Attack with using Sparse Coder

Mohamadzadeh,

Ghayedi,

Pasban

et al. 2023

IJE

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One of the most serious causes of disease in the world's population, which kills many people worldwide every year, is heart attack. Various factors are involved in this matter, such as high blood pressure, high cholesterol, abnormal pulse rate, diabetes, etc. Various methods have been proposed in this field, but in this article, by using sparse codes in the classification process, higher accuracy has been achieved in predicting heart attacks. The proposed method consists of two parts: preprocessing and sparse code processing. The proposed method is resistant to noise and data scattering because it uses a sparse representation for this purpose. The spars allow the signal to be displayed at its lowest value, which leads to improve computing speed and reduce storage requirements. To evaluate the proposed method, the Cleveland database has been used, which includes 303 samples and each sample has 76 features. Only 13 features are used in the proposed method. FISTA, AMP, DALM and PALM classifiers have been used for the classification process. The accuracy of the proposed method, especially with the PALM classifier, is the highest among other classifiers with 96.23%, and the other classifiers are 95.08%, 94.11% and 94.52% for DALM, AMP, FISTA, respectively.

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Data mining techniques with machine learning algorithm to predict patients of heart disease

Cited by 11 publications

References 3 publications

Comparative Analysis of Restock Needs Bottled Water Using K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and the Naïve Bayes Algorithm

Comparative Analysis of Restock Needs Bottled Water Using K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and the Naïve Bayes Algorithm

Implementasi Teknik Data Mining Pada Data Kunjungan Wisatawan Menggunakan K-Means Clustering Di Hotel Bintang Kota Cirebon

Algorithm of Predicting Heart Attack with using Sparse Coder

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