Data-Driven Diabetes Risk Factor Prediction Using Machine Learning Algorithms with Feature Selection Technique

Kakoly, Israt Jahan; Hoque, Md. Rakibul; Hasan, Najmul

doi:10.3390/su15064930

Cited by 8 publications

(8 citation statements)

References 35 publications

(46 reference statements)

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“…The prevalence of type 2 diabetes has been rising worldwide. Diabetes lowers quality of life and promotes early mortality; it is linked to numerous other disorders, including those of the kidney, heart, lower limb, and eye [1,13].…”

Section: Background To the Studmentioning

confidence: 99%

“…However, the study falls short in including key parameters like specificity and the area under the receiver operating characteristic curve (AUC-ROC), which are essential for a comprehensive assessment of the model's performance. [1] develop a system to predict diabetes risk variables by employing machine learning techniques.…”

Section: Improving Patient Outcomes Depends On Type II Diabetes Early...mentioning

confidence: 99%

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A Logistic Regression-Based Technique for Predicting Type II Diabetes

Babatunde,

Balogun

et al. 2024

AIMS Research Journal

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In recent years, diabetes has emerged as one of the main causes of death for people. The spread of unhealthy foods, sedentary lifestyles, and eating habits have all contributed to the annual increase in the incidence of diabetes. A diabetes prediction model can help with clinical management decision-making. Diabetes prevention may be aided by being aware of potential risk factors and early detection of high-risk individuals. Numerous diabetes prediction models have been created. The size of the data set to be used was an issue in earlier research, but more recent studies have incorporated the use of high-quality, trustworthy data sets, such as the Vanderbilt and PIMA India data sets. Recent research has demonstrated that a few variables, including glucose, pregnancy, body mass index (BMI), the function of the diabetic pedigree, and age, can be used to predict Type II diabetes. Machine learning models of these parameters can be used to accurately predict the chance of the disease occurring as it was investigated in this study. In order to predict Type II diabetes, this study used the machine learning method Logistic Regression. Keywords: Type II Diabetes, Logistic Regression, Hyperparameter and Prediction ACity FCSI Journal Citation Format Babatunde, R.S., Babatunde, A.N., Balogun, B.F., Abdulrahman, T.A., Umar, E., Ajiboye, R.A., Mohammed, S.B., Oke, A.A. & Obiwusi, K.Y. (2024): A Logistic Regression-Based Technique for Predicting Type II Diabetes. Journal of Computational Sciences & Informatics. Academic City University College, Accra, Ghana. Vol 4 No. 1 March, 2024 Pp 1-14. https://www.isteams.net/ghanabespoke2023. dx.doi.org/10.22624/AIMS/FCSIJ/2024/P1

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Section: Background To the Studmentioning

confidence: 99%

Section: Improving Patient Outcomes Depends On Type II Diabetes Early...mentioning

confidence: 99%

A Logistic Regression-Based Technique for Predicting Type II Diabetes

Babatunde,

Balogun

et al. 2024

AIMS Research Journal

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“…The algorithm was able to achieve higher risk-adjusted returns than traditional portfolio optimization methods. In addition to using reinforcement learning algorithms for trading and portfolio optimization, there have also been studies exploring the use of reinforcement learning for other tasks in finance, such as credit risk assessment [11] and fraud detection [12]. Despite the promising results of these studies, there are still challenges to using reinforcement learning in finance.…”

Section: IIImentioning

confidence: 99%

Quantitative Trading using Deep Q Learning

Sarkar¹

2023

IJRASET

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Reinforcement learning (RL) is a branch of machine learning that has been used in a variety of applications such as robotics, game playing, and autonomous systems. In recent years, there has been growing interest in applying RL to quantitative trading, where the goal is to make profitable trades in financial markets. This paper explores the use of RL in quantitative trading and presents a case study of a RLbased trading algorithm. The results show that RL can be a powerful tool for quantitative trading, and that it has the potential to outperform traditional trading algorithms. The use of reinforcement learning in quantitative trading represents a promising area of research that can potentially lead to the development of more sophisticated and effective trading systems. Future work could explore the use of alternative reinforcement learning algorithms, incorporate additional data sources, and test the system on different asset classes. Overall, our research demonstrates the potential of using reinforcement learning in quantitative trading and highlights the importance of continued research and development in this area. By developing more sophisticated and effective trading systems, we can potentially improve the efficiency of financial markets and generate greater returns for investors

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“…These techniques are mainly used to predict and classify variables of interest (supervised learning) through feature selection. [48][49][50] Both processes are subfields of data mining. 51,52 However, the terminology associated with cluster 2 highlights the relationship between DL techniques (a subfield of AI and machine learning 53,54 ) and diabetes complications related to eye diseases.…”

Section: Author Abstract and Trending Topicsmentioning

confidence: 99%

“…These techniques are mainly used to predict and classify variables of interest (supervised learning) through feature selection . 48-50 Both processes are subfields of data mining. 51,52…”

Section: Network Analysismentioning

confidence: 99%

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

García-Jaramillo,

Luque,

León-Vargas

2023

J Diabetes Sci Technol

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Background: The use of machine learning and deep learning techniques in the research on diabetes has garnered attention in recent times. Nonetheless, few studies offer a thorough picture of the knowledge generation landscape in this field. To address this, a bibliometric analysis of scientific articles published from 2000 to 2022 was conducted to discover global research trends and networks and to emphasize the most prominent countries, institutions, journals, articles, and key topics in this domain. Methods: The Scopus database was used to identify and retrieve high-quality scientific documents. The results were classified into categories of detection (covering diagnosis, screening, identification, segmentation, among others), prediction (prognosis, forecasting, estimation), and management (treatment, control, monitoring, education, telemedicine integration). Biblioshiny and RStudio were used to analyze the data. Results: A total of 1773 articles were collected and analyzed. The number of publications and citations increased substantially since 2012, with a notable increase in the last 3 years. Of the 3 categories considered, detection was the most dominant, followed by prediction and management. Around 53.2% of the total journals started disseminating articles on this subject in 2020. China, India, and the United States were the most productive countries. Although no evidence of outstanding leadership by specific authors was found, the University of California emerged as the most influential institution for the development of scientific production. Conclusion: This is an evolving field that has experienced a rapid increase in productivity, especially over the last years with exponential growth. This trend is expected to continue in the coming years.

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Data-Driven Diabetes Risk Factor Prediction Using Machine Learning Algorithms with Feature Selection Technique

Cited by 8 publications

References 35 publications

A Logistic Regression-Based Technique for Predicting Type II Diabetes

A Logistic Regression-Based Technique for Predicting Type II Diabetes

Quantitative Trading using Deep Q Learning

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

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