Risk Assessment of Type 2 Diabetes Mellitus Prediction using an Improved Combination of NELM-PSO

Reddy, Shiva Shankar; Mahesh, G

doi:10.4108/eai.3-5-2021.169579

Cited by 4 publications

(4 citation statements)

References 33 publications

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“…Reddy Shiva, et al [179] introduced a unique approach to diabetes prediction by employing a hybrid method combining PSO with an Artificial Fish Swarm Algorithm. This study also performed data preprocessing, including missing value imputation using the mean and normalization using min-max.…”

Section: Optimizationmentioning

confidence: 99%

“…Sensitive to initial configuration [174] AFS Efficient in optimization problems Does not guarantee an optimal solution [179] with the disease.…”

Section: Stackingmentioning

confidence: 99%

“…Applying other combinations of feature selection, re-sampling, and cost-sensitive learning methods [156]. Explore the side effects that can cause diabetes [179]. Evaluate the model using other datasets and risk factors that influence chronic disease [183].…”

Section: Open Issues and Future Workmentioning

confidence: 99%

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Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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According to the World Health Organization (WHO), some chronic diseases such as diabetes mellitus, stroke, cancer, cardiac vascular, kidney failure, and hypertension are essential for early prevention. One of the prevention that can be taken is to predict chronic diseases using machine learning based on personal medical record or general checkup result. The common prediction objective is to minimize the prediction error as low as possible. The most influencing chronic diseases prediction factors are the quality of data and the choice of predictor such as machine learning methods. The five main problems those lower data quality are outliers, missing values, feature selection, normalization, and imbalance. After we ensure the quality of data, the next task is to choose the best machine learning methods. The most influencing factor to consider when we choose the predictor its performance evaluation (accuracy, recall, precision, f1-score). Thus, predicting chronic disease aims to produce increased performance and solve problems in medical data. This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. This paper covers machine learning methods discussion such as supervised learning, ensemble learning, deep learning, and reinforcement learning. The preprocessing handling we discuss includes missing values, outliers, feature selection, normalization, and imbalance. The final discussions of this paper are open issues, and the potential future works in improving the prediction performance for chronic diseases using a data preprocessing handling and machine learning methods.

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Section: Optimizationmentioning

confidence: 99%

“…Sensitive to initial configuration [174] AFS Efficient in optimization problems Does not guarantee an optimal solution [179] with the disease.…”

Section: Stackingmentioning

confidence: 99%

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Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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“…The patient, once discharged from the hospital, are readmitted with multiple causes [23], and they predict whether the patients are diagnosed clearly or not [24] by using ML and DL models [25]. The NELM particle swarm optimization (PSO) algorithm indicated whether the DM patient was affected by BC [26]. Gopal et al [27] propose a method for conducting early BCdiagnostics utilizing the IoTand ML.…”

Section: Related Workmentioning

confidence: 99%

An extensible framework for recurrent breast cancer prognosis using deep learning techniques

Reddy¹,

Swaroop²,

Voosala³

et al. 2023

IJEECS

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<span lang="EN-US">Due to population growth, early illness detection is getting more challenging. Breast cancer is the second-deadliest malignancy. An estimated one million people are newly diagnosed with the disease annually in India. Most cases are never diagnosed because they are either ignored or not reported. Also, secondary malignancies may develop after a breast cancer recurrence, including those of the brain, lungs, and bones. Early detection and treatment of people with recurring breast cancer may help prevent secondary cancers and other disorders. By examining cell and tumour data as well as data from other diseases, this project hopes to overcome this obstacle and more accurately diagnose breast cancer. Accurate diagnosis of breast cancer may be achieved with the use of machine learning techniques. The effort focuses on recurring breast cancer and aims to efficiently identify it. In ensemble learning, decision trees filter out non-essential qualities. Cancer recurrences and non-recurrences are distinguished using voting classifiers. The soft voting classifier classifies a variety of data sets with 98.24% accuracy. The proposed model has an accuracy of 0.97, a recall of 0.97, an F1-Score of 0.969, and a Choen kappa score of 0.9655, as stated by the recommended model.</span>

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AI-Based Diagnostic Test Prediction- An Effective Way to Handle the New Normal Era in Healthcare

More,

Singh Saini,

Kumar Sharma

2023

2023 6th International Conference on Contemporary Computing and Informatics (IC3I)

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Risk Assessment of Type 2 Diabetes Mellitus Prediction using an Improved Combination of NELM-PSO

Cited by 4 publications

References 33 publications

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

An extensible framework for recurrent breast cancer prognosis using deep learning techniques

AI-Based Diagnostic Test Prediction- An Effective Way to Handle the New Normal Era in Healthcare

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