Patient Diabetes Forecasting Based on Machine Learning Approach

Shukla, Arvind Kumar

doi:10.1007/978-981-15-4032-5_91

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…However, one possible limitation of the study is the relatively small size of the dataset used. Shukla (2020) predicted the occurrence of diabetes by employing a linear regression model.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning Algorithm for Diabetes Prediction in Maiduguri, Borno State

Dada,

Birma,

Gora

2024

Mikailalsys J. of Math. and Statistics

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Diabetes mellitus (DM) is a metabolic disease characterised by high levels of glucose in the blood, known as hyperglycemia, that can result in multiple problems within the body. The World Health Organisation (WHO) data for 2021 reveals a substantial increase in the prevalence of diabetes mellitus (DM), with the number of cases rising from 108 million in 1980 to 422 million in 2014. Between 2000 and 2019, there was a 3% increase in mortality rates associated with diabetes, categorised by age. In 2019, DM caused the deaths of more than 2 million people. These concerning figures clearly necessitate an immediate response. An alarming incidence of diabetes among the population of Maiduguri and Borno State inspired this investigation. This research proposed stacking ensemble learning approach to predict the rate of occurrence of diabetes cases in Maiduguri. The paper used different types of regression models to predict the occurrences of diabetes cases in Maiduguri over time. These models included adaptive boosting regression (Adaboost), gradient boosting regression (GBOOST), random forest regression (RFR), ordinary least square regression (OLS), least absolute shrinkage selection operator regression (LASSO), and ridge regression (RIDGE). The performance indicators studied in this work are root mean square (RMSE), mean absolute error (MAE), and mean square error (MSE). These metrics were used to assess the effectiveness of both the machine learning and proposed Stacking Ensemble Learning (SEL) approaches. Performance metrics considered in this study are root mean square (RMSE), mean absolute error (MAE), and mean square error (MSE), which were used to evaluate the performance of the machine learning and the proposed Stacking Ensemble Learning (SEL) technique. Experimental results revealed that SEL is a better predictor compared to other machine learning approaches considered in this work with an RMSE of 0.0493; a MSE of 0.0024; and a MAE of 0.0349. It is hoped that this research will help government officials understand the threat of diabetes and take the necessary mitigation actions.

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“…However, one possible limitation of the study is the relatively small size of the dataset used. Shukla (2020) predicted the occurrence of diabetes by employing a linear regression model.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning Algorithm for Diabetes Prediction in Maiduguri, Borno State

Dada,

Birma,

Gora

2024

Mikailalsys J. of Math. and Statistics

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“…Specifically, regarding early diabetes detection, ML has emerged as a promising approach for the detection of diabetes and its complications. One specific example is the study conducted by Shukla (2020) in which a Logistic Regression algorithm was used to predict the risk of developing diabetes in Indian adults using demographic and clinical variables, such as glucose, body mass index (BMI), and pregnancies. The study reported a high accuracy rate of 82.92%, suggesting that the developed model could identify individuals at risk of developing diabetes and potentially prevent its onset [15].…”

Section: Introductionmentioning

confidence: 99%

“…One specific example is the study conducted by Shukla (2020) in which a Logistic Regression algorithm was used to predict the risk of developing diabetes in Indian adults using demographic and clinical variables, such as glucose, body mass index (BMI), and pregnancies. The study reported a high accuracy rate of 82.92%, suggesting that the developed model could identify individuals at risk of developing diabetes and potentially prevent its onset [15]. Another example is the study by Islam et al (2020) that employed serval ML algorithms, including Naive Bayes, Logistic Regression, and Random Forest algorithms, to predict the risk of diabetes in a sample of 520 individuals.…”

Section: Introductionmentioning

confidence: 99%

The Utilization of Machine Learning Algorithms for Assisting Physicians in the Diagnosis of Diabetes

Nguyen

Tung²,

Nguyen

et al. 2023

Diagnostics

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This paper investigates the use of machine learning algorithms to aid medical professionals in the detection and risk assessment of diabetes. The research employed a dataset gathered from individuals with type 2 diabetes in Ninh Binh, Vietnam. A variety of classification algorithms, including Decision Tree Classifier, Logistic Regression, SVC, Ada Boost Classifier, Gradient Boosting Classifier, Random Forest Classifier, and K Neighbors Classifier, were utilized to identify the most suitable algorithm for the dataset. The results of the present study indicate that the Random Forest Classifier algorithm yielded the most promising results, exhibiting a cross-validation score of 0.998 and an accuracy rate of 100%. To further evaluate the effectiveness of the selected model, it was subjected to a testing phase involving a new dataset comprising 67 patients that had not been previously seen. The performance of the algorithm on this dataset resulted in an accuracy rate of 94%, especially the study’s notable finding is the algorithm’s accurate prediction of the probability of patients developing diabetes, as indicated by the class 1 (diabetes) probabilities. This innovative approach offers a meticulous and quantifiable method for diabetes detection and risk evaluation, showcasing the potential of machine learning algorithms in assisting clinicians with diagnosis and management. By communicating the diabetes score and probability estimates to patients, the comprehension of their disease status can be enhanced. This information empowers patients to make informed decisions and motivates them to adopt healthier lifestyle habits, ultimately playing a crucial role in impeding disease progression. The study underscores the significance of leveraging machine learning in healthcare to optimize patient care and improve long-term health outcomes.

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“…Shukla [ 18 ] used a LR algorithm, took out a dataset that showed the maximum accuracy would be yielded if parameters such as glucose, body mass index (BMI), and pregnancies, were used, which were represented in the form of a bar chart, as shown in Fig. 2 .…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review of machine learning techniques on diabetes detection

Sharma

Shah

2021

Vis. Comput. Ind. Biomed. Art

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Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors. Data mining techniques with algorithms such as - density-based spatial clustering of applications with noise and ordering points to identify the cluster structure, the use of machine vision systems to learn data on facial images, gain better features for model training, and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners. Machine learning classifiers such as support vector machines, logistic regression, and decision trees, have been comparative discussed various authors. Deep learning models such as artificial neural networks and recurrent neural networks have been considered, with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models. Various parameters such as the root-mean-square error, mean absolute errors, area under curves, and graphs with varying criteria are commonly used. In this study, challenges pertaining to data inadequacy and model deployment are discussed. The future scope of such methods has also been discussed, and new methods are expected to enhance the performance of existing models, allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.

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Patient Diabetes Forecasting Based on Machine Learning Approach

Cited by 5 publications

References 6 publications

Ensemble Machine Learning Algorithm for Diabetes Prediction in Maiduguri, Borno State

Ensemble Machine Learning Algorithm for Diabetes Prediction in Maiduguri, Borno State

The Utilization of Machine Learning Algorithms for Assisting Physicians in the Diagnosis of Diabetes

A comprehensive review of machine learning techniques on diabetes detection

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