Blog IJ-AI Teknologi

IAES Nawala: Penggunaan data mining

Salam, rekan Nawala! Semoga kalian selalu dalam keadaan sehat.

Ini adalah IAES Nawala dari Institute of Advanced Engineering and Science. Hari ini kami akan berbagi kabar tentang penggunaan data mining. Data mining adalah proses menemukan pengetahuan yang menarik dari sejumlah besar data. Teknik data mining ini dapat digunakan untuk meningkatkan operasional dan pengambilan keputusan yang lebih baik. Panthong dan Wongkanthiya (2023) melakukan penelitian yang menggunakan teknik data mining untuk menganalisis kondisi kesehatan lansia. Hasil yang diperoleh menunjukkan ada empat kelompok lansia dengan karakteristik yang berbeda. Temuan ini dapat digunakan untuk mengembangkan intervensi kesehatan yang sesuai untuk setiap kelompok tersebut.

Data survey on the elderly health condition in each year aimed to investigate the performance result on the elderly health care and to evaluate the elderly’s health and health promotion. Thus, in analyzing the data, it mainly relied on the mining data technique for the evaluating health condition. This study presented the data analysis by clustering method. Then, the data was taken from each group to find the association rule. The analysis results showed that the elderly’s health condition data could be classified into four different groups; cluster 1 (25%) were male elderly with high blood pressure and smoking cigarette, cluster 2 (25%) were female elderly with no the congenital disease but the result from the eye sight examination, it was found that they were long-sighted, cluster 3 (24%) were female elderly with no the congenital disease but having the insomnia and osteoarthritis and cluster 4 (26%) were female elderly with high blood pressure and diabetes. It also indicated that each group had the rule showing the correlation between the data in each group having the minimum value of confidence at 0.8 and the minimum value of support not less than 0.5.

Analysis of clustering and association using data mining technique for elderly health condition dataset
Rattanawadee Panthong, Thawin Wongkanthiya

Disisi lain menurut penelitian Shanshool (2023), teknik data mining dapat digunakan untuk diagnosis dini dan pengobatan penyakit arteri koroner (CAD). Metode data mining yang digunakan adalah decision tree (DT), logistic regression (LR), random forest (RF), dan Naïve Bayes (NB). Hasil yang didapatkan menunjukkan bahwa metode NB memiliki akurasi terbaik sebesar 89,47%. Penelitian ini dapat membantu diagnosis CAD yang akurat, cepat, dan efisien.

Recent healthcare reports indicate clearly an increasing mortality rates worldwide which puts a significant burden on the healthcare sector due to different diseases. Coronary artery diseases (CAD) is one of the main reasons of these uprising death rates since it affects the heart directly. For early diagnosis and treatment of CADs, a swiftly growing technology called data mining has been used to collect and categorize necessary data from patients; age, blood sugar and pressure, a type of thorax pain, cholesterol, and so on. Therefore, this paper adopted four data mining methods; decision tree (DT), logistic regression (LR), random forest (RF), and Naïve Bayes (NB) to achieve the goal. The paper utilized the Cleveland dataset along with Python programming language to compare among the four data mining methods in terms of precision, accuracy, recall, and area under the curve. The results illustrated that NB method has the best accuracy of 89.47% compared with previous studies which will help with accurate, fast and inexpensive diagnosis of CADs.

Comparison of various data mining methods for early diagnosis of human cardiology
Abeer Mohammed Shanshool, Enas Mohammed Hussien Saeed, Hasan Hadi Khaleel

Selanjutnya penggunaan data mining adalah untuk meningkatkan diagnosis diabetes tipe 2, dimana dapat menghasilkan diagnosis yang lebih cepat dan efisien. Dengan menggunakan algoritma random forest, sebuah model mencapai akurasi 90,43% dan diintegrasikan ke dalam aplikasi web. Penelitian ini menunjukkan peningkatan yang signifikan dalam hal waktu diagnosis, biaya, dan tingkat kesulitan.

Currently, type 2 diabetes mellitus is one of the world’s most prevalent diseases and has claimed millions of people’s lives. The present research aims to know the impact of the use of machine learning in the diagnostic process of type 2 diabetes mellitus and to offer a tool that facilitates the diagnosis of the dis-ease quickly and easily. Different machine learning models were designed and compared, being random forest was the algorithm that generated the model with the best performance (90.43% accuracy), which was integrated into a web platform, working with the PIMA dataset, which was validated by specialists from the Peruvian League for the Fight against Diabetes organization. The result was a decrease of (A) 88.28% in the information collection time, (B) 99.99% in the diagnosis time, (C) 44.42% in the diagnosis cost, and (D) 100% in the level of difficulty, concluding that the application of machine learning can significantly optimize the diagnostic process of type 2 diabetes mellitus.

Predictive machine learning applying cross industry standard process for data mining for the diagnosis of diabetes mellitus type 2
Victor Garcia-Rios, Marieta Marres-Salhuana, Fernando Sierra-Liñan, Michael Cabanillas-Carbonell

Beberapa artikel di atas merupakan bagian kecil dari penelitian mengenai penggunaan data mining. Untuk mendapatkan informasi lebih lanjut, pembaca dapat mengunjungi laman IAES International Journal of Artificial Intelligence (IJ-AI) dan membaca artikel secara GRATIS melalui tautan berikut https://ijai.iaescore.com/.

Redaksi: I. Busthomi