Naïve Bayes and K-Nearest Neighbor Algorithms Performance Comparison in Diabetes Mellitus Early Diagnosis

Haviluddin, .; Puspitasari, Novianti; Burhandeny, Aji Ery; Nurulita, Andi Dhiya Awalia; Trahutomo, Dinnuhoni

doi:10.3991/ijoe.v18i15.34143

Cited by 3 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This extensive data fuels the training of machine learning algorithms, facilitating the discovery of concealed patterns and relationships [8,9]. This innovative approach holds the promise for scientists to construct forecasting models of diabetes, devise tailored treatments, and advance the frontiers of medical research [10]. In this context, diabetes has emerged as a focal point, contributing a crucial dimension to the dynamic intersection between machine learning and medical insight.…”

Section: Related Workmentioning

confidence: 99%

Diabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction

Aouragh,

Bahaj,

Toufik

2024

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Diabetes, a pervasive global health concern, presents diagnostic challenges due to its nuanced onset and far-reaching implications. Traditional diagnostic approaches, reliant on time-consuming assessments, necessitate a paradigm shift towards more efficient methodologies. In response, this study introduces a diagnostic support system leveraging the power of optimized machine learning algorithms. Addressing class imbalance within a dataset comprising 768 records, our methodology intricately weaves together feature selection, dimensionality reduction techniques, and grid search optimization. Specifically, the Extra Trees model, fine-tuned via grid search, emerges as the most potent, showcasing remarkable performance metrics: an accuracy score of 92.5%, an F1-score of 93.7%, and an AUC-ROC of 92.47%. These findings underscore the pivotal role of machine learning in reshaping diabetes diagnosis, offering transformative possibilities for global healthcare enhancement.

show abstract

Section: Related Workmentioning

confidence: 99%

Diabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction

Aouragh,

Bahaj,

Toufik

2024

Int. J. Onl. Eng.

View full text Add to dashboard Cite

show abstract

“…The wrapper method incorporates a learning algorithm in assessing network traffic features [5]. Naïve Bayes is a learning model [45]. The k-means algorithm is able to categorize a dataset into classes [46], and in this case, it will categorize the network traffic according to clusters based on the closest averages [5].…”

Section: Application Of the Crisp-dm To Applied It Problemmentioning

confidence: 99%

Comparing Metaheuristic Search Techniques in Addressing the Effectiveness of Clustering-Based DDoS Attack Detection Methods

Zeinalpour,

McElroy

2024

Electronics

View full text Add to dashboard Cite

Distributed Denial of Service (DDoS) attacks have increased in frequency and sophistication over the last ten years. Part of the challenge of defending against such attacks requires the analysis of very large volumes of data. Metaheuristic algorithms can assist in selecting relevant features from the network traffic data for use in DDoS detection models. By efficiently exploring different combinations of features, these methods can identify subsets that are informative for distinguishing between normal and attack traffic. However, identifying an optimized solution in this area is an open research question. Tuning the parameters of metaheuristic search techniques in the optimization process is critical. In this study, a switching approximation is used in a variety of metaheuristic search techniques. This approximation is used to find the best solution for the analysis of the network traffic features in either lower or upper values between 0 and 1. We compare the fine-tuning of this parameter against standard approaches and find that it is not substantially better than the BestFirst algorithm (a standard default approach for feature selection). This study contributes to the literature by testing and eliminating various fine-tuning strategies for the metaheuristic approach.

show abstract

“…Different machine learning (ML) algorithms can be used to recognize and classify the disease. We need carbohydrate-rich foods to maintain healthy physical and mental equilibrium in diabetics [2][3][4]. Diabetics have excessive levels of glucose in their blood in addition to urine, which can cause serious complications such as blindness and failure of kidneys over time.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Multi-Layer Perceptron using Variable Step Size Firefly Optimization Algorithm for Diabetes Data Classification

Behera¹,

Sarangi²,

Mishra³

et al. 2023

Int. J. Onl. Eng.

View full text Add to dashboard Cite

According to a survey conducted by the International Diabetes Federation, the proportion of people living with diabetes is gradually rising. Diabetes mellitus is a chronic disorder caused by elevated blood sugar levels. For the early diagnosis and treatment of diabetes patients, efficient machine-learning methods are needed. Data Classification is a significant subject in many areas of life, and it is also a very challenging job in data mining. Clinical data mining has recently gained attention in complicated healthcare challenges relying on healthcare datasets. The principal objective of classification is to classify all data in a given dataset to a certain class label. In the healthcare field, classification is commonly employed in much research articles. A hybrid method for diabetes data classification is suggested by integrating multilayer perceptron with a modified firefly optimization algorithm for diabetes data classification. The performance of the proposed hybrid multilayer perceptron variable step size firefly algorithm is compared with other hybrid models such as the hybrid multilayer perceptron particle swarm optimization algorithm, hybrid multilayer perceptron differential evolution algorithm, and hybrid multilayer perceptron firefly optimization algorithm. The performance of these models is calculated based on accuracy, precision, recall, F1 score, and mean square error. In comparison to other models, the proposed hybrid model produces superior outcomes for diabetes data classification.

show abstract

Naïve Bayes and K-Nearest Neighbor Algorithms Performance Comparison in Diabetes Mellitus Early Diagnosis

Cited by 3 publications

References 15 publications

Diabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction

Diabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction

Comparing Metaheuristic Search Techniques in Addressing the Effectiveness of Clustering-Based DDoS Attack Detection Methods

Optimizing Multi-Layer Perceptron using Variable Step Size Firefly Optimization Algorithm for Diabetes Data Classification

Contact Info

Product

Resources

About