Future Prospective of Soft Computing Techniques in Psychiatric Disorder Diagnosis

Sharma, Manik; Romero, Natalia

doi:10.4108/eai.30-7-2018.159798

Cited by 13 publications

(6 citation statements)

References 33 publications

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“…The fog computing is used to store and manage the resources. The opinion mining, mental or psychiatric issues are addressed using machine learning with high accuracy [41,18]. Information security in big data is recently addressed using blockchain technologies [32,39,23,35].…”

Section: Review Of Literaturementioning

confidence: 99%

Deep Learning Based Cardiovascular Disease Risk Factor Prediction Among Type 2 Diabetes Mellitus Patients

Selvarathi¹,

Varadhaganapathy²

2023

ITC

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Type 2 Diabetes Mellitus (T2DM) is a common chronic disease that is caused due to insulin discharge disorder. Due to the complication of T2DM, the outcomes of this disease lead to severe illness, death and cardiovascular disease (CVD). Given a larger number of diabetes patients, it is necessary to find the patients with a high risk of CVD complications. For this, the traditional methods are not sufficient and it is important to develop a deep learning-based efficient quantitative model to predict the risk of CVD among diabetes patients. The major objective of this research is to assess the efficient artificial intelligence approach toward the proposal of a personalized deep learning model that can able to predict the risk of fatal and non-fatal CVD among T2DM patients. First, the unbalanced dataset is preprocessed to make the dataset balanced for processing. Second, the features are reduced and important features are selected using Rank based Feature Importance (RFI) model which will improve the prediction accuracy. Third, the proposed Cascaded Convolution Graph LSTM (CCGLSTM) has been used as a classifier to predict the risk of CVD. Novelty of the work resides on ranking based feature analysis is cascaded with CGLSTM. The proposed model is implemented and experimented with various evaluation metrics using the data from 560 patients of five-year follow-up with T2DM. These evaluated results are compared with the state of-the-art methods and the proposed model is proven to be superior to other approaches in terms of AUC (0.989), Accuracy (98.8%), recall (96.7%), precision (96.8%), specificity (97.4%) and F1-Score (97.5%).

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Section: Review Of Literaturementioning

confidence: 99%

Deep Learning Based Cardiovascular Disease Risk Factor Prediction Among Type 2 Diabetes Mellitus Patients

Selvarathi¹,

Varadhaganapathy²

2023

ITC

View full text Add to dashboard Cite

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“…Sharma et al [24] presented a review on supervised learning and soft computing techniques used in stress diagnosis of human beings, further discussing their contributions, pros and cons respectively. Sharma et al [25] discussed the need to investigate opportunity and challenges in the diagnosis of psychiatric disorders using soft computing techniques as 20% of the youth suffers from one or the other psychiatric disorders as per reports of World Federation of Mental Health in 2018. Sharma et al [26] proposed a model to examine the performance of swarm intelligence techniques for effective diagnosis of cardiac arrhythmia, concluding satin-bird optimization as highly accurate technique.…”

Section: Related Workmentioning

confidence: 99%

Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

Dhruv¹,

Mittal²,

Modi³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Pancreatic cancer is highly lethal as it grows, spreads rapidly and difficult to diagnose at its early stages. It can be identified through scan images. The tumorous images obtained from imaging techniques suffer from the drawback of cryptic data due to presence of unwanted noise and poor contrast. OBJECTIVE: To reduce the risk of pancreatic cancer, its detection and diagnosis at an early stage becomes crucial. METHODS: The proposed work encompasses the processing of CT scans of pancreatic tumor using classical and artificial intelligence based optimized edge detection techniques for optimization and detection of tumor. RESULTS: The simulation results are highly encouraging as evident from the far improved visibility of resultant images with Particle Swarm Optimization. CONCLUSION: The output image with PSO shows the quality enhanced CT images which helps in accurate detection and diagnosis of the pancreatic tumor at an early stage providing an aid in medical imaging.

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“…Finally, the combined weighted score is used for predicting user behaviour. In [11] authors M. Sharma and N. Romero presented "Future prospective of soft computing techniques to diagnose psychiatric disorder". They also emphasize using various deep learning techniques for better results and fast processing.…”

Section: Related Workmentioning

confidence: 99%

Cervix Image Classification for Prognosis of Cervical Cancer using Deep Neural Network with Transfer Learning

Dhawan¹,

Singh²,

Arora³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Cervical cancer is the leading cancer among the other female cancers. It develops in the cervix of women. It takes decades in development thus can be preventable if diagnosed at an early stage. The cervix is classified into three types Type I/II/III. The efficacy of the treatment depends on the diagnosis of the right type of cervix. There is a thin line difference between the three types. Thus, identification of the right type of cervix becomes a difficult task for the health care providers too. To aid this problem, we proposed an algorithm based on the standard transfer learning approach used for building a model that classifies cervix images. OBJECTIVES: The objective of this study is to develop a cervical cancer predictive model based on deep learning and transfer learning techniques that will recognize and classify the cervix images into one of the classes (Type 1/Type2/Type3). METHODS: Techniques used for carrying out the experimental work includes deep learning and Transfer Learning. The three pertained models namely InceptionV3, ResNet50, and VGG19 are used for creating ConvNet that will classify the cervix images. RESULTS: The result of the experiment reveals that the Inception v3 model is performing better than Vgg19 and ResNet50 with an accuracy of 96.1% on the cervical cancer dataset. CONCLUSION: In the future, augmentation techniques can be employed to achieve better accuracy.

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Future Prospective of Soft Computing Techniques in Psychiatric Disorder Diagnosis

Abstract: -

Cited by 13 publications

References 33 publications

Deep Learning Based Cardiovascular Disease Risk Factor Prediction Among Type 2 Diabetes Mellitus Patients

Deep Learning Based Cardiovascular Disease Risk Factor Prediction Among Type 2 Diabetes Mellitus Patients

Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

Cervix Image Classification for Prognosis of Cervical Cancer using Deep Neural Network with Transfer Learning

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