Grid Search Hyperparameter Analysis in Optimizing The Decision Tree Method for Diabetes Prediction DOI Creative Commons

Desi Anggreani,

Hamdani,

Nurmisba Nurmisba

et al.

Indonesian Journal of Data and Science, Journal Year: 2024, Volume and Issue: 5(3), P. 190 - 197

Published: Dec. 31, 2024

Diabetes is a global health issue that continues to rise, especially in Indonesia, caused by unhealthy lifestyles, poor diets, and genetic factors. Early detection of diabetes risk crucial prevent serious complications, machine learning offers innovative predictive solutions. This research focuses on the development prediction model using Decision Tree algorithm with hyperparameter optimization through Grid Search technique. The methodology includes collection patient medical data key attributes such as glucose levels, blood pressure, skin health, insulin, body mass index (BMI), pedigree, age, history. tuning process carried out varying parameters maximum tree depth (max_depth), minimum number samples required split node (min_samples_split), at leaf (min_samples_leaf). used systematically explore combinations order find optimal configuration can improve model's performance. preprocessing, splitting dataset into training testing sets, training, evaluation accuracy metrics, confusion matrix, ROC AUC curve. initial results show 76%, which was then improved 81% after Search. visualization decision reveals levels BMI have most significant contributions predicting risk. demonstrates potential supporting early diabetes, showing promising capabilities. Nevertheless, further larger datasets integration other algorithms highly recommended generalization model. main contribution this learning-based approach assist personnel screening for more efficiently accurately.

Language: Английский

Optimization of Artificial Intelligence Algorithm Selection: PIPRECIA-S Model and Multi-Criteria Analysis DOI Open Access
Stefan Popović, Dejan Viduka, Ana Bašić

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(3), P. 562 - 562

Published: Jan. 30, 2025

In the age of digitization and ever-present use artificial intelligence (AI), it is essential to develop methodologies that enable systematic evaluation ranking different AI algorithms. This paper investigated application PIPRECIA-S model as a methodological framework for multi-criteria Analyzing relevant criteria such efficiency, flexibility, ease implementation, stability scalability, provided comprehensive overview existing algorithms identified their strengths weaknesses. The research results showed enabled structured objective assessment, which facilitated decision-making in selecting most suitable specific applications. approach not only advances understanding but also contributes development strategies implementation various industries.

Language: Английский

Citations

0
Artificial Intelligence and Internet of Things Integration in Pharmaceutical Manufacturing: A Smart Synergy DOI Creative Commons

Reshma Kodumuru,

S. Sarkar,

Varun Parepally

et al.

Pharmaceutics, Journal Year: 2025, Volume and Issue: 17(3), P. 290 - 290

Published: Feb. 22, 2025

Background: The integration of artificial intelligence (AI) with the internet things (IoTs) represents a significant advancement in pharmaceutical manufacturing and effectively bridges gap between digital physical worlds. With AI algorithms integrated into IoTs sensors, there is an improvement production process quality control for better overall efficiency. This facilitates enabling machine learning deep real-time analysis, predictive maintenance, automation—continuously monitoring key parameters. Objective: paper reviews current applications potential impacts integrating concert technologies like cloud computing data analytics, within sector. Results: Applications discussed herein focus on industrial analytics quality, underpinned by case studies showing improvements product reductions downtime. Yet, many challenges remain, including ethical implications AI-driven decisions, most all, regulatory compliance. review also discusses recent trends, such as drug discovery blockchain traceability, intent to outline future autonomous manufacturing. Conclusions: In end, this points basic frameworks that illustrate ways overcome existing barriers increased efficiency, personalization, sustainability.

Language: Английский

Citations

0
Investigation of ML algorithms for prediction of CFD data of fluid flow inside a packed-bed reactor DOI Creative Commons
Yujiang Qiu, Pengfei Liu

Case Studies in Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 106093 - 106093

Published: April 1, 2025

Language: Английский

Citations

0
BustedURL: Collaborative Multi-agent System for Real-Time Malicious URL Detection DOI

Jayaprakash Nariyambut Sundarraj,

Yan Zhang,

Santosh Kapil Dev Itharaju

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 463 - 476

Published: Dec. 12, 2024

Language: Английский

Citations

0
Grid Search Hyperparameter Analysis in Optimizing The Decision Tree Method for Diabetes Prediction DOI Creative Commons

Desi Anggreani,

Hamdani,

Nurmisba Nurmisba

et al.

Indonesian Journal of Data and Science, Journal Year: 2024, Volume and Issue: 5(3), P. 190 - 197

Published: Dec. 31, 2024

Diabetes is a global health issue that continues to rise, especially in Indonesia, caused by unhealthy lifestyles, poor diets, and genetic factors. Early detection of diabetes risk crucial prevent serious complications, machine learning offers innovative predictive solutions. This research focuses on the development prediction model using Decision Tree algorithm with hyperparameter optimization through Grid Search technique. The methodology includes collection patient medical data key attributes such as glucose levels, blood pressure, skin health, insulin, body mass index (BMI), pedigree, age, history. tuning process carried out varying parameters maximum tree depth (max_depth), minimum number samples required split node (min_samples_split), at leaf (min_samples_leaf). used systematically explore combinations order find optimal configuration can improve model's performance. preprocessing, splitting dataset into training testing sets, training, evaluation accuracy metrics, confusion matrix, ROC AUC curve. initial results show 76%, which was then improved 81% after Search. visualization decision reveals levels BMI have most significant contributions predicting risk. demonstrates potential supporting early diabetes, showing promising capabilities. Nevertheless, further larger datasets integration other algorithms highly recommended generalization model. main contribution this learning-based approach assist personnel screening for more efficiently accurately.

Language: Английский

Citations

0