An Optimization Approach to Improve Classification Performance in Cancer and Diabetes Prediction

Helal, Mustakim Al; Chowdhury, Atiqul Islam; Islam, Ashraful; Ahmed, Eshtiak; Mahmud, Md. Swakshar; Hossain, Sabrina

doi:10.1109/ecace.2019.8679413

Cited by 16 publications

(5 citation statements)

References 7 publications

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“…Based on several studies, we found that a commonly used dataset for health data mining was the Pima Indians Diabetes Dataset from the University of California, Irvine (UCI) Machine Learning Database [24][25][26][27][28][29]. The datasets consist of several medical predictor (independent) variables and one target (dependent) variable, Outcome.…”

Section: Methodsmentioning

confidence: 99%

“…In order to check the performance of the upgraded network has been processedt the experimental dataset of [23,24], representing a good dataset for testing LSTM neural network. The experimental dataset [24] has been adopted in the literature for different data mining testing [24][25][26][27][28][29]. Specifically in reference [25], the K-means algorithm has been applied for predicting diabetes, in reference [26] some authors applied synthetic data in order to balance a machine learning dataset model, while references [27][28][29] have analyzed different machine learning algorithms for diabetes prediction.…”

Section: Introductionmentioning

confidence: 99%

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LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction

Massaro¹,

Maritati²,

Giannone³

et al. 2019

Applied Sciences

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The paper is focused on the application of Long Short-Term Memory (LSTM) neural network enabling patient health status prediction focusing the attention on diabetes. The proposed topic is an upgrade of a Multi-Layer Perceptron (MLP) algorithm that can be fully embedded into an Enterprise Resource Planning (ERP) platform. The LSTM approach is applied for multi-attribute data processing and it is integrated into an information system based on patient management. To validate the proposed model, we have adopted a typical dataset used in the literature for data mining model testing. The study is focused on the procedure to follow for a correct LSTM data analysis by using artificial records (LSTM-AR-), improving the training dataset stability and test accuracy if compared with traditional MLP and LSTM approaches. The increase of the artificial data is important for all cases where only a few data of the training dataset are available, as for more practical cases. The paper represents a practical application about the LSTM approach into the decision support systems (DSSs) suitable for homecare assistance and for de-hospitalization processes. The paper goal is mainly to provide guidelines for the application of LSTM neural network in type I and II diabetes prediction adopting automatic procedures. A percentage improvement of test set accuracy of 6.5% has been observed by applying the LSTM-AR- approach, comparing results with up-to-date MLP works. The LSTM-AR- neural network can be applied as an alternative approach for all homecare platforms where not enough training sequential dataset is available.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction

Massaro¹,

Maritati²,

Giannone³

et al. 2019

Applied Sciences

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“…The SVM classifier shows 75.65% accuracy. Al Helal, et al (2019) developed three categorization models which are the KNN, Naïve Bayes, and RF then their final accuracy was according to 66.19%, 72.66%, 73.72%. They were used in the Weka tool.…”

Section: Related Workmentioning

confidence: 99%

Makine Öğrenimi Sınıflandırma Algoritmalarını Kullanarak Diyabet Tahmini

NAHZAT¹,

Yağanoğlu²

2021

European Journal of Science and Technology

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Artificial intelligence's use in health systems has evolved substantially in recent years. In medical diagnosis, machine learning (ML) has a wide variety of uses. Machine learning techniques are used to forecast or diagnose a variety of life-threatening illnesses, including cancer, diabetes, heart disease, thyroid disease, and so on. Chronic diabetes is one of the most common diseases worldwide and making the diagnosis process simpler and quicker would have a huge effect on the treatment process. The fundamental goal of this work is to prepare and carry out diabetes prediction using various machine learning techniques and Conduct output analysis of those techniques to find the best classifier with the highest accuracy. This study examines diabetes prediction by taking different diabetes disease-related attributes. We use the Pima Indian Diabetes Dataset and applied the Machine Learning classification methods like K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree (DT) for diabetes prediction. The models used in this analysis have various degrees of accuracy. This study shows a model that can correctly forecast diabetes. In comparison to other machine learning methods, the random forest has high accuracy in forecasting diabetes, according to the findings of this study.

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“…The accuracy of the SVM model was 75.65%. Al Helal, Mustakim, et al [10] constructed three classification models are the KNN, Naïve Bayes, and RF then their final accuracy was according to 66.19%, 72.66%, 73.72%. They were used in the Weka tool.…”

Section: Related Workmentioning

confidence: 99%

Gradient Boosting Classification kullanarak Diabetes Mellitus Tahmini

Fatema

Uzbaş

Baykan

2020

European Journal of Science and Technology

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Diabetes has become a pervasive and endemic health problem worldwide. It is a chronic disease and also life-threatening. It can cause health problems in many organs such as the heart, kidneys, eyes, nerves, and blood vessels. To reduce the fatality rate from diabetes, early prevention techniques are needed. Nowadays, machine learning techniques are used to predict or detect different life-threatening diseases like cancer, diabetes, heart diseases, thyroid, etc. In this study, a prediction model of diabetes mellitus was presented using the Pima Indian dataset. Three different machine learning techniques that Decision Tree (DT), Random Forest (RF) and, Gradient Boosting (GB) algorithm were used to predict diabetes mellitus and the performance analysis was performed. Confusion matrix, accuracy, F1 score, precision, recall, Cohen's kappa were evaluated and also a ROC curve was plotted. Out of the three techniques, the best results have been achieved with GB.

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An Optimization Approach to Improve Classification Performance in Cancer and Diabetes Prediction

Cited by 16 publications

References 7 publications

LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction

LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction

Makine Öğrenimi Sınıflandırma Algoritmalarını Kullanarak Diyabet Tahmini

Gradient Boosting Classification kullanarak Diabetes Mellitus Tahmini

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