A neuro fuzzy approach for the diagnosis of postpartum depression disorder

Osubor, Veronica I.; Egwali, Annie O

doi:10.1007/s42044-018-0021-6

Cited by 11 publications

(7 citation statements)

References 13 publications

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“… Heart disease is one of the most common chronic diseases and causes of adult death worldwide [ 33 ]. Health, Medical Education Department has announced that 33–38 percent of deaths in the country are due to cardiovascular disease, and Iran has the highest rate of heart death in the world.…”

Section: Resultsmentioning

confidence: 99%

“…In 2020, Verma et al [ 32 ] proposed a newly proposed method of hybrid feature selection technique for evaluating the performance of base learners, and we find that the reduced data subset performed is higher than the whole data set. Also, Osubor et al [ 33 ] used an adaptive fuzzy neural inference system to predict postpartum depression. Thirty-six data samples were used in model training.…”

Section: Related Workmentioning

confidence: 99%

“…Thirty-six data samples were used in model training. This system had a training error of 7.0706e − 005 in period one and an average test error of 3.0185 [ 33 ].…”

Section: Related Workmentioning

confidence: 99%

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A hybrid method for heart disease diagnosis utilizing feature selection based ensemble classifier model generation

Abdollahi

Nouri-Moghaddam

2022

Iran J Comput Sci

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Heart disease is one of the most complicated diseases, and it affects a large number of individuals throughout the world. In healthcare, particularly cardiology, early and accurate detection of cardiac disease is critical. The Heart Disease Data Set-UCI repository collects data on heart disease. The search space and complexity of the classification models are increased by this raw dataset, which contains redundant and inconsistent data. We need to eliminate the redundant and unnecessary elements from the data to improve classification accuracy. As a consequence, feature selection approaches might be useful for reducing the cost of diagnosis by identifying the most important qualities. This research developed an ensemble classification model based on a feature selection approach in which selected features play a role in classification. Accordingly, a classification approach was introduced using ensemble learning with a genetic algorithm, feature selection, and biomedical test values to diagnose heart disease. Based on the results, it is deduced that the benefits of using the feature selection method vary depending on the utilized machine learning technique. However, the best-proposed model based on the combination of genetic algorithm and the ensemble learning model has achieved an accuracy of 97.57% on the considered datasets. The suggested diagnosis system achieved better accuracy than previously proposed methods and can easily be implemented in healthcare to identify heart disease. Graphical abstract

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A hybrid method for heart disease diagnosis utilizing feature selection based ensemble classifier model generation

Abdollahi

Nouri-Moghaddam

2022

Iran J Comput Sci

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“…Machine learning techniques have been applied to diagnosing depression but most of the systems designed failed to perform feature selection and extraction on symptoms that consist of the dataset used for a depression diagnosis. This has reduced the classification accuracy of the machine learning algorithm [14,21]. Although researchers have observed that the diagnosis of clinical depression has ignored optimal training, feature selection, and feature extraction [14,21].…”

Section: Introductionmentioning

confidence: 99%

“…This has reduced the classification accuracy of the machine learning algorithm [14,21]. Although researchers have observed that the diagnosis of clinical depression has ignored optimal training, feature selection, and feature extraction [14,21]. Therefore, it is imperative to design a model for diagnosing clinical depression, which performs feature selection and extraction on the dataset to be used for training the learning algorithm of the design system.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression

Adegboye¹,

Agboizebeta²

2021

MLR

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Depression is a serious illness that affects millions each year and if left untreated, it may lead to the deaths of many. It comes in many flavors that can be very different among people and this makes diagnosing it very difficult. A lot of artificial intelligence systems have been designed to diagnosing depression but they failed to perform feature selection and extraction on the dataset used in training the systems and this has a huge implication on the classification accuracy of the system. The objective of this research work is to develop a depression diagnosis system, that takes into consideration feature selection and extraction of dataset using Genetic-Neuro-Fuzzy techniques. Feature selection and extraction, will enable identification of key symptoms and hidden traits which are vital in diagnosis of depression. In this work, a Genetic Neuro-Fuzzy Model which is capable of handling feature selection and extraction on depression dataset was proposed and designed for diagnosing clinical depression. The GA component optimizes the clinical dataset which consist of series of diagnosed depression cases by performing feature selection and extraction, while ANFIS is used in training the optimized dataset obtained from the GA. The system had 92.5% prediction accuracy. This is a significant improvement over the best related model in literature that achieved a prediction accuracy of 92.4%. The system is recommended for psychiatrist hospital to aid in depression diagnosis. The research is limited to the diagnosis of clinical depression; future work should focus on the other forms of depression and treatments. The model has incorporated feature selection and feature extraction for the prediction of clinical depression with significant results established with performance indicators.

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