Diabetes Mellitus Prediction Through Interactive Machine Learning Approaches

Panda, Rabinarayan; Dash, Sachikanta; Padhy, Sasmita; Das, Rajendra Kumar

doi:10.1007/978-981-19-1412-6_12

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…The HIgh REsolution Slope Stability Simulator (HIRESSS) was used to model the outcomes, and then it was compared to a conventional, non-IoT based technique. www.ijacsa.thesai.org Models for analysis of submerged pools [30], use of random field Gaussian methods [31], blocky structure systems [32], near-surface rock strength slope analysis [33], and reinforcement learning models [34][35][36][37] are also used for high efficiency slope analysis. It can be observed that most of these models do not define sensor placement analysis, due to which their efficiency is limited.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Slope Stability in Open Cast Mines via Machine Learning based IoT Framework

Das¹,

Pani²,

Samal³

et al. 2022

IJACSA

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Slope stability has been a matter of concern for most geologists, mainly due to the fact that unstable slopes cause a greater number of accidents, which in turn reduces efficiency of mining operations. In order to reduce the probability of these slope instabilities, methods like tension crack mapping, inclinometer measurements, time domain reflectometry, borehole extensometers, piezometer, radar systems and image processing systems are deployed. These systems work efficiently for single site slope failures, but as the number of mining sites increase, dependency of one site slope failure on nearby sites also increases. Current systems are not able to capture this data, due to which the probability of accidents at open cast mines increases. In order to reduce this probability, a high efficiency internet of things (IoT) based continuous slope monitoring and control system is designed. This system assists in improving the efficiency of real-time slope monitoring via usage of a sensor array consisting of radar, reflectometer, inclinometer, piezometer and borehole extensometer. All these measurements are given to a high efficiency machine learning classifier which uses data mining, and based on its output suitable actions are taken to reduce accidents during mining. This information is dissipated to nearby mining sites in order to inform them about any inconsistencies which might occur due to the slope changes on the current site. Results were simulated using HIgh REsolution Slope Stability Simulator (HIRESSS), and an efficiency improvement of 6% is achieved for slope analysis in open cast mines, while probability of accident reduction is increased by 35% when compared to traditional non-IoT based approach.

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Section: Literature Reviewmentioning

confidence: 99%

Improving Slope Stability in Open Cast Mines via Machine Learning based IoT Framework

Das¹,

Pani²,

Samal³

et al. 2022

IJACSA

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“…Individuals with diabetes mellitus experience impaired meal absorption, resulting in elevated blood glucose levels [4,5]. Diabetes is a medical disorder characterized by either insufficient production of insulin (type 1 diabetes) or impaired utilization of hormones (type 2 diabetes) [6][7][8]. In type 1 diabetes, the body ceases the production of insulin.…”

Section: Introductionmentioning

confidence: 99%

Optimized Deep Belief Networks Based Categorization of Type 2 Diabetes using Tabu Search Optimization

Panigrahy,

Dash,

Padhy

2024

IJACSA

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Diabetics mellitus has the potential to result in numerous challenges. Based on the increasing morbidity rates in recent years, it is projected that the global diabetic population will surpass 642 million by 2040, indicating that approximately one in every ten individuals will have diabetes. Undoubtedly, this alarming statistic necessitates urgent focus from both academics as well as industry to foster novelty and advancement in prediction of diabetics, with the aim of preserving patients' lives. Deep learning (DL) was employed to forecast a multitude of ailments as a result of its swift advancement. Nevertheless, DL approaches continue to face challenges in achieving optimal prediction performance as a result of the selection of hyperparameters and tuning of parameters. Hence, the careful choice of hyper-parameters plays a crucial role in enhancing classification performance. This paper introduces TSO-DBN, a Tabu Search Optimization method (TSO) that is based on Deep Belief Network (DBN). TSO-DBN has demonstrated exceptional performance in several medical fields. The Tabu Search Optimization algorithm (TSO) has been used to pick hyperparameters and optimize parameters. During the experiment, two problems were tackled in order to improve the findings. The TSO-DBN model exhibited exceptional performance, surpassing other models with an accuracy of 96.23%, an F1-score of 0.8749, and a Matthews Correlation Coefficient (MCC) of 0.88.63.

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“…It is a visual detection of diseases by analyzing a huge amount of image data, processing and interpreting them to understand the structural changes of the lesion sites of patients. Machine learning [7][8][9][10] This paper makes the following contributions: Section 2 discusses literature survey, section 3 explains about the proposed approach, Section 4 discusses about the experimental setup, Section 5 discusses concludes.…”

Section: Introductionmentioning

confidence: 99%

Identification of Polyp from Colonoscopy Images by Deep Belief Network based Polyp Detector Integration Model

Dash,

Padhy

et al. 2023

EAI Endorsed Trans Perv Health Tech

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Cancer is a disease involving unusual cell growth likely to spread to other parts of the body. According to WHO 2020 report, colorectal malignancy is the globally accepted second leading cause of cancer related deaths. Colorectal malignancy arises when malignant cells often called polyp, grow inside the tissues of the colon or rectum of the large intestine. Colonoscopy, CT scan, Histopathological analysis are some manual approaches of malignancy detection that are time consuming and lead to diagnostic errors. Supervised CNN data model requires a large number of labeled training samples to learn parameters from images. In this study we propose an expert system that can detect the colorectal malignancy and identify the exact polyp area from complex images. In this approach an unsupervised Deep Belief Network (DBN) is applied for effective feature extraction and classification of images. The classified image output of DBN is utilized by Polyp Detector. Residual network and feature extractor components of Polyp Detector helps polyp inspector in pixel wise learning. Two stage polyp network (PLPNet) is a R-CNN architecture with two stage advantage. The first stage is the extension of R-CNN to detect the polyp lesion area through a location box also called Polyp Inspector. Second Stage performs polyp segmentation. Polyp Inspector transfers the learned semantics to the polyp segmentation stage. It helps to enhance the ability to detect polyp with improved accuracy and guide the learning process. Skip schemes enrich the feature scale. Publicly available CVC-Clinical DB and CVC Colon DB datasets are used for experiment purposes to achieve a better prediction capability for clinical practices.

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Diabetes Mellitus Prediction Through Interactive Machine Learning Approaches

Cited by 6 publications

References 12 publications

Improving Slope Stability in Open Cast Mines via Machine Learning based IoT Framework

Improving Slope Stability in Open Cast Mines via Machine Learning based IoT Framework

Optimized Deep Belief Networks Based Categorization of Type 2 Diabetes using Tabu Search Optimization

Identification of Polyp from Colonoscopy Images by Deep Belief Network based Polyp Detector Integration Model

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