Application of market–basket analysis on healthcare

Rao, Ashish K.; Kiran, Jammula Surya; Poornalatha, G

doi:10.1007/s13198-021-01298-2

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…In retail, MBA has been applied to analyze customer purchases and recommend products based on their shopping habits. In healthcare, MBA has been used to identify potential drug interactions and recommend treatment plans for patients (Rao, A. B, 2021).…”

Section: Market Basket Analysismentioning

confidence: 99%

Unveiling the Wave: Unraveling Sales Trends and Unlocking Market Basket Insights

Teh,

Lim,

Chin

2023

ijmst

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This article presents sales trends and market basket analysis for Food and Beverages (F&B) items recommendation using linear regression time series and association rules. The purpose of this study is to help businesses to make better informed decisions by understanding their customers’ purchasing behaviors and the sales trends of the F&B items. This includes the study of both cross- selling and up-selling patterns using market basket analysis. To improve the market basket analysis, food name clustering has been carried out to group those foods having similarity ratio higher or equal to 80 based on the Levenshtein distances between the food names. This study analyzes sales data from a mobile app platform that register the Malaysian F&B hawkers (or merchants) and app users (or buyers) who sell and purchase F&B respectively. This study is based on sales trends analysis and association rules of product recommendations by examining FP-Growth and the relationship between product items bought. This study provides valuable insights for local Malaysian hawkers who are looking to improve their F&B items recommendations and to make better data-driven decisions while planning their marketing activities. It helps to improve sales by either up or cross-sell their products more effectively.

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Section: Market Basket Analysismentioning

confidence: 99%

Unveiling the Wave: Unraveling Sales Trends and Unlocking Market Basket Insights

Teh,

Lim,

Chin

2023

ijmst

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“…Asosiasi, clustering, klasifikasi, dan prediksi adalah beberapa teknik Data Mining yang sering digunakan [5], [12], [13]. Proses Data Mining ini meliputi pembersihan data, integrasi data, pemilihan data, transformasi data, penambangan data, evaluasi pola, dan representasi pengetahuan [14].…”

Section: Pendahuluanunclassified

Akurasi dalam Analisis Kompetensi Calon Tenaga Keperawatan Menggunakan Algoritma Apriori

Habib-ur-Rahman¹

2022

INFEB

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Setiap lulusan S1 keperawatan (NERS) harus dinyatakan kompeten pada Uji Kompetensi Ners Indonesia (UKNI) yang diadakan Kemdikbud untuk mendapatkan Surat Tanda Registrasi (STR) sebagai syarat bekerja. Tingkat kelulusan mahasiswa keperawatan dalam mengikuti UKNI sangat rendah jika dibandingkan dengan beberapa negara lain. Penelitian ini bertujuan mengidentifikasi faktor-faktor yang perlu dioptimalkan calon tenaga keperawatan dalam persiapan menghadapi UKNI dengan teknologi data mining. Data yang diolah dalam penelitian ini bersumber dari Appskep Indonesia, suatu startup yang bergerak di bidang pendidikan kesehatan, diantaranya try out dan bimbingan belajar UKNI. Appskep memiliki data kelulusan UKNI lebih dari 2000 pesertanya yang tersebar di seluruh Indonesia pada tahun 2021 dan 2022. Data tersebut dianalisis menggunakan Algoritma Apriori untuk menemukan rule-rule asosiasi yang terkait dengan kelulusan calon tenaga perawat dalam UKNI. Hasil dari pengolahan data peserta UKNI ini adalah ditemukannya beberapa set rule asosiasi yang mempengaruhi kompeten atau tidaknya calon tenaga perawat dalam UKNI. Rule asosiasi yang paling dominan adalah jika penguasaan materi keperawatan jiwa lebih dari 60 persen maka seorang perawat akan lulus pada UKNI, dengan support 55% dan confidence 100%.

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“…Market basket analysis (MBA) is a strategy that can be used to discover relationships between items purchased by customers [1]. MBA is one of the methods in data mining that focuses on how to find purchasing patterns by extracting sales transaction data.…”

Section: Introductionmentioning

confidence: 99%

A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm

2022

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In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using the simple additive weighting (SAW) approach. Products with the two highest-ranking values are then coupled with product data that has been purchased and examined in the SPM using the generalized sequential pattern (GSP) method. The GSP algorithm produces rules in a sequence pattern, which are then utilized to construct product suggestions. According to the test results, product suggestions derived from a mix of sales transaction data and consumer behavior data outperform product recommendations generated just from sales transaction data. Precision, recall, and F-measure metrics values rose by 185.46, 170.83, and 178.43%, respectively.

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Application of market–basket analysis on healthcare

Cited by 12 publications

References 17 publications

Unveiling the Wave: Unraveling Sales Trends and Unlocking Market Basket Insights

Unveiling the Wave: Unraveling Sales Trends and Unlocking Market Basket Insights

Akurasi dalam Analisis Kompetensi Calon Tenaga Keperawatan Menggunakan Algoritma Apriori

A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm

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