Implementation of K-Means Algorithm to Classify Instagram Reels and Carousel Content Performance Based

2026-06-01T02:01:25+00:00

With the increasing popularity of digital marketing, Instagram became one of the top platforms where the audience can be reached. It is important to gain an insight into the performance of different types of contents to ensure that the marketing efforts bear fruit. This research will apply the K-Means algorithm to classify Instagram Reels and Carousel contents based on performance by taking into account such factors as likes, comments, shares, and saves. For the purposes of the study, the data were collected from a variety of accounts both personal and of a business nature. The number of clusters was defined by the Elbow Method, after which they were categorized depending on their performance such as high, medium, and low. The results indicate that the classification based on performance provided by the K-Means algorithm can provide insights into marketing practices on Instagram. Consequently, the present research will contribute to the development of digital marketing studies, particularly in the area of content analysis, within the field of data mining.

RadOnco-Priority: Machine Learning Decision Support for Radiotherapy Queue Prioritization Using Real-World Retrospective Radiotherapy Referral Data

2026-06-14T09:04:07+00:00

Radiotherapy queues are often managed by referral date and manual clinician judgment, although limited linear accelerator capacity requires prioritization that is clinically transparent, operationally auditable, and fair. This study evaluates RadOnco-Priority, a machine learning-enabled decision support framework for radiotherapy queue prioritization, using a de-identified real-world retrospective dataset of 240 radiotherapy referral records rather than simulated or synthetic patient records. The system combines a literature-informed rule-based urgency score with supervised machine learning models to identify patients requiring accelerated booking. Accelerated booking need was defined a priori as an operational triage label reflecting clinician-documented priority, urgent symptoms, time-sensitive tumor-site and treatment-intent combinations, accumulated referral delay, and planning complexity. Logistic regression, random forest, and gradient boosting were trained to predict accelerated booking need, while a capacity-aware scheduling simulation evaluated waiting-time redistribution. To address potential circularity, additional ablation analyses were performed with the aggregate urgency score removed from the predictors. In the held-out test set, logistic regression achieved the highest discrimination in the full-feature model (AUC 0.91), with sensitivity-oriented classification favoring reduced false negatives. Performance remained acceptable after removing the aggregate urgency score, indicating that the model did not rely solely on the pre-specified scoring logic. The scheduling simulation reduced median waiting time in the high-priority group and decreased the proportion of high-priority patients waiting more than 28 days. These findings support RadOnco-Priority as an interpretable, human-governed information-system framework for radiotherapy queue management. Prospective multicenter validation, fairness monitoring, and local ethics approval remain required before routine implementation.

SAGA: Journal of Technology and Information System

Implementation of K-Means Algorithm to Classify Instagram Reels and Carousel Content Performance Based

RadOnco-Priority: Machine Learning Decision Support for Radiotherapy Queue Prioritization Using Real-World Retrospective Radiotherapy Referral Data