A Fuzzy Rule-Based System for Classification of Diabetes

Article Computer Science, Information Systems

Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus

Chandrashekhar Azad et al.

Summary: The study introduced a novel prediction model, PMSGD, and through different layers of processing, it improved the accuracy and effectiveness of diabetes classification.

MULTIMEDIA SYSTEMS (2022)

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Article Computer Science, Information Systems

A comparison of machine learning algorithms for diabetes prediction

Jobeda Jamal Khanam et al.

Summary: The study utilized data mining, machine learning algorithms, and neural network methods to predict diabetes, using the Pima Indian Diabetes dataset. It was found that the logistic regression and support vector machine models performed well in diabetes prediction, and the neural network model with two hidden layers provided 88.6% accuracy.

ICT EXPRESS (2021)

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Article Chemistry, Analytical

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Parvathaneni Naga Srinivasu et al.

Summary: Deep learning models, specifically MobileNet V2 and LSTM, have been proposed for efficient classification of skin diseases, showing higher accuracy and effectiveness compared to other state-of-the-art models. The use of a grey-level co-occurrence matrix helps assess diseased growth progression, with the proposed method outperforming others with over 85% accuracy.

SENSORS (2021)

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Article Computer Science, Information Systems

eDiaPredict: An Ensemble-based Framework for Diabetes Prediction

Ashima Singh et al.

Summary: This article introduces an ensemble-based framework called eDiaPredict, which utilizes different machine learning algorithms to predict diabetes status among patients with high accuracy. The use of modern computational intelligence in healthcare, particularly in predicting disease onset and recurrence, identifying survival analysis biomarkers, etc., highlights the importance of machine learning techniques in the medical field.

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS (2021)

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Article Chemistry, Analytical

A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification

Moumita Mandal et al.

Summary: In this study, a feature selection framework for disease detection based on medical reports was proposed, which utilized a tri-stage process involving ensemble of filter methods, classification algorithms, Pearson correlation, and a meta-heuristic algorithm. The results showed that the proposed method outperformed state-of-the-art methods in detecting important features, leading to less medical tests for accurate diagnosis and saving time and cost.

SENSORS (2021)

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Article Engineering, Electrical & Electronic

Efficient diabetes mellitus prediction with grid based random forest classifier in association with natural language processing

Asma Ahmed Abokhzam et al.

Summary: The increasing prevalence of diabetes has led to the proposal of using machine learning for efficient diagnosis. By utilizing the ML Grid Search algorithm and the Pima Indian Diabetic Dataset, a diabetes prediction method with an accuracy of approximately 95.7% has been suggested, providing users with the ability to understand results in multiple languages.

INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY (2021)

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Article Computer Science, Artificial Intelligence