Future Prospective of Soft Computing Techniques in Psychiatric Disorder Diagnosis

Sharma, Manik; Romero, Natalia

doi:10.4108/eai.30-7-2018.159798

Cited by 11 publications

(5 citation statements)

References 33 publications

(34 reference statements)

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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

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“…This approach is used for balancing the energy of consecutive sensing and transmitting signal toward the sink. In order to diagnosis, Sharma et al (34) presented the opportunities and challenges for diagnosis of psychiatric disorder using soft computing algorithm. From the literature survey, it has been concluded that there are numerous protocols in WBAN for health monitoring purposes.…”

Section: Related Workmentioning

confidence: 99%

An Energy Efficient Health Monitoring System using Relay Node in Wireless Body Area Networks

Bilandi¹,

Verma²,

Dhir³

2020

EAI Endorsed Transactions on Pervasive Health and Technology

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INTRODUCTION: In today's world, Wireless Body Area Networks (WBAN) has emanated as a budding field within the healthcare scenario for monitoring, detecting, predicting, and diagnosing various fatal diseases such as cancer, cardiovascular, diabetes and other infectious diseases such as tuberclosis, pneumonia and ebola. It is an assemblage of bio-sensors which could be put on or embedded in a patient's body to surveil the functionality of various body organs. OBJECTIVES: The purpose of that proposed protocol is to select the relay node and record the various physiological parameters like body temperature, blood pressure, rate of blood sugar of human being. Energy consumption is a major hurdle in WBAN as replacing as well as recharging the nodes pinioned to human body may bring about discomfort to the patient. Therefore, in this work, the emphasis is given to improve the performance of WBAN i.e. network lifetime, greater mobility, stability and energy efficiency. METHODS: To find the shortest energy-efficient route and avoid any kind of relay node failure, a novel routing scheme called Enhanced Opportunistic Protocol using Relay nodes (E-OPR) is proposed for data routing. CONCLUSION: Finally, an enhanced opportunistic protocol using relay node in WBAN has been proposed for cost effective real time health monitoring system. The proposed solution seems to give better results as compare single-hop, multi-hop routing protocols in terms of throughput, network lifetime, energy consumption and path loss.

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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

2018

EAI Endorsed Transactions on Pervasive Health and Technology

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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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Future Prospective of Soft Computing Techniques in Psychiatric Disorder Diagnosis

Cited by 11 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

An Energy Efficient Health Monitoring System using Relay Node in Wireless Body Area Networks

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

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