Predicting the outcome of heart failure against chronic-ischemic heart disease in elderly population – Machine learning approach based on logistic regression, case to Villa Scassi hospital Genoa, Italy

Stojanov, Done; Lazarova, Elena; Veljkova, Elena; Rubartelli, Paolo

doi:10.1016/j.jksus.2023.102573

Cited by 14 publications

(9 citation statements)

References 30 publications

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“…The logistic regression algorithm was used as a baseline classification algorithm. Logistic regression is the most suitable method for the analysis of binary classification tasks with the high diagnostic ability [ 14 ]; ANN was used as the main algorithm and accommodated several features, such as age, exercise, diet, blood glucose testing, formal education, diabetes duration, and HbA1c levels. Logistic regression models have been employed to solve this type of problem and enhance patients’ diabetes self-management assessment [ 26 ].…”

Section: Resultsmentioning

confidence: 99%

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Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes

et al. 2023

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The use of Artificial intelligence in healthcare has evolved substantially in recent years. In medical diagnosis, Artificial intelligence algorithms are used to forecast or diagnose a variety of life-threatening illnesses, including breast cancer, diabetes, heart disease, etc. The main objective of this study is to assess self-management practices among patients with type 2 diabetes in rural areas of Pakistan using Artificial intelligence and machine learning algorithms. Of particular note is the assessment of the factors associated with poor self-management activities, such as non-adhering to medications, poor eating habits, lack of physical activities, and poor glycemic control (HbA1c %). The sample of 200 participants was purposefully recruited from the medical clinics in rural areas of Pakistan. The artificial neural network algorithm and logistic regression classification algorithms were used to assess diabetes self-management activities. The diabetes dataset was split 80:20 between training and testing; 80% (160) instances were used for training purposes and 20% (40) instances were used for testing purposes, while the algorithms’ overall performance was measured using a confusion matrix. The current study found that self-management efforts and glycemic control were poor among diabetes patients in rural areas of Pakistan. The logistic regression model performance was evaluated based on the confusion matrix. The accuracy of the training set was 98%, while the test set’s accuracy was 97.5%; each set had a recall rate of 79% and 75%, respectively. The output of the confusion matrix showed that only 11 out of 200 patients were correctly assessed/classified as meeting diabetes self-management targets based on the values of HbA1c < 7%. We added a wide range of neurons (32 to 128) in the hidden layers to train the artificial neural network models. The results showed that the model with three hidden layers and Adam’s optimisation function achieved 98% accuracy on the validation set. This study has assessed the factors associated with poor self-management activities among patients with type 2 diabetes in rural areas of Pakistan. The use of a wide range of neurons in the hidden layers to train the artificial neural network models improved outcomes, confirming the model’s effectiveness and efficiency in assessing diabetes self-management activities from the required data attributes.

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Section: Resultsmentioning

confidence: 99%

“…Deep learning models, recurrent neural networks, and genetic algorithms play an important role in Artificial Intelligence applications. AI is ideal for detecting, analyzing, and predicting heart disease [ 14 ], diabetes complications [ 15 ], breast cancer [ 16 ], hepatitis B [ 17 ], and COVID-19 severity [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes

et al. 2023

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“…For the cluster, we use the sum of the squared Euclidean distances between each data point x i and the centroid m k of the subset C k , which contains x i . Euclidean distance was chosen due to its popularity, but we emphasize that other possibilities exist in the literature [56][57][58][59].…”

Section: Experiments Designmentioning

confidence: 99%

“…Discriminant analysis (LDA) was applied to check the possibility of forecasting the indicators generated by the experiments. In practice, LDA will compare the discriminating potential of each indicator [59][60][61]. The prediction errors in a supervised approach are used as a metric to check the randomness of the results of the experiments.…”

Section: Experiments Designmentioning

confidence: 99%

Dynamic Equilibrium of Sustainable Ecosystem Variables: An Experiment

Perroni

Veiga

et al. 2023

Sustainability

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Developing indicators to monitor the dynamic equilibrium of sustainable ecosystem variables has been challenging for policymakers, companies, and researchers. The new method matrix decomposition analysis (MDA) is an adaptation of the Leontief input–output equations for the disaggregated structural decomposition of key performance indicators (KPI). The main problem that this work addresses is related to the behavior of MDA when compared to traditional methodologies such as data envelopment analysis (DEA) and stochastic frontier analysis (SFA). Can MDA be considered robust enough for wide applicability? To compare the models, we developed a methodology called marginal exponentiation experiments. This approach is a type of simulation that raises the inputs and outputs of an entity to a marginal power, thus making it possible to compare a large number of models with the same data. RMarkdown was used for methodological operationalization, wherein data science steps are coded in specific chunks, applying a layered process with modeling. The comparison between the models is operationalized in layers using techniques such as descriptive statistics, correlation, cluster, and linear discriminant analysis (LDA). Given the results, we argue that MDA is a Leontief partial equilibrium model that produces indicators with dual interpretation, enabling the measurement of the dynamic equilibrium of sustainable ecosystem variables. Furthermore, the method offers a new ranking system that detects relative changes in the use of resources correlated with efficiency analysis. The practical value for decision-makers relates to the fact that we found evidence that MDA can be considered robust enough to identify whether a given ecosystem is in equilibrium and that the excessive use of resources or abnormal productivity can cause instability.

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“…Deep learning is only one type of machine learning classification algorithm, and each classification algorithm has its advantages and disadvantages [2]. Taking the medical field as an example, Done Stojanov et al used a logistic regression algorithm for risk prediction of cardiovascular diseases and achieved the best distinction between heart failure and chronic ischemic heart disease outcomes [3]. Mostafa Langarizadeh et al used a plain Bayesian network for disease prediction and found that disease prediction based on the plain Bayesian network had the best performance in most diseases [4].…”

Section: Introductionmentioning

confidence: 99%

A TEDE Algorithm Studies the Effect of Dataset Grouping on Supervised Learning Accuracy

Wang

Song

et al. 2023

Electronics

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Datasets are the basis for research on deep learning methods in computer vision. The impact of the percentage of training sets in a dataset on the performance of neural network models needs to be further explored. In this paper, a twice equal difference enumeration (TEDE) algorithm is proposed to investigate the effect of different training set percentages in the dataset on the performance of the network model, and the optimal training set percentage is determined. By selecting the Pascal VOC dataset and dividing it into six different datasets from largest to smallest, and then dividing each dataset into the datasets to be analyzed according to five different training set percentages, the YOLOv5 convolutional neural network is used to train and test the 30 datasets to determine the optimal neural network model corresponding to the training set percentages. Finally, tests were conducted using the Udacity Self-Driving dataset with a self-made Tire Tread Defects (TTD) dataset. The results show that the network model performance is superior when the training set accounts for between 85% and 90% of the overall dataset. The results of dataset partitioning obtained by the TEDE algorithm can provide a reference for deep learning research.

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Predicting the outcome of heart failure against chronic-ischemic heart disease in elderly population – Machine learning approach based on logistic regression, case to Villa Scassi hospital Genoa, Italy

Cited by 14 publications

References 30 publications

Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes

Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes

Dynamic Equilibrium of Sustainable Ecosystem Variables: An Experiment

A TEDE Algorithm Studies the Effect of Dataset Grouping on Supervised Learning Accuracy

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