Early Diagnosis Of Chronic Disease Research Articles

Real-time Predictive Health Monitoring using Ai-driven Wearable Sensors: Enhancing Early Detection and Personalized Interventions in Chronic Disease Management

The use of artificial Intelligence in wearable health sensor has brought a great change in the way chronic diseases are being managed since it monitors the health and gives prompt prediction of diseases coupled with recommendations. This paper seeks to investigate the part played by AI wearables in augmenting early detection & proactive plans relating to chronic illnesses, including diabetics, cardiovascular diseases, and hypertension. Implicitly, the purpose of this study is to update the current understanding of the subject by examining the effect that predictive analytics has on patient outcomes and cost. Technologically, the concepts of the research rely on the data collected from different kinds of wearable devices and analyze by machine learning and real time analytic to examine the abnormal of vital signs before symptoms occur. Analysis is carried out on the outcomes which reveals a 25 percent decrease in hospitalization incidences per early diagnosis of chronic diseases and a 30 percent enhancement in the adherence to treatment provided formed by precautions embraced from various sensors data. This research also increases the stock of knowledge on the role of artificial intelligence in healthcare by presenting empirical evidence regarding the use of wearable devices in chronic diseases. The uniqueness of this concept is in the synchrony of monitoring processes with the application of the predictive model in providing proactive health care solutions. Some potential future research and development considerations in AI-assisted technologies in healthcare are also described.

The impact of artificial intelligence on early diagnosis of chronic diseases in rural areas

The impact of artificial intelligence on early diagnosis of chronic diseases in rural areas

Sentiment Analysis on Article Blog Post as an Application of NLP Chatbot

Within artificial intelligence, natural language processing is a relatively recent topic. With an estimated eight billion digital voice assistants in use due to their popularity, some of the most well-known instances of natural language processing in action include Apple Siri, Amazon Alexa, and, more recently, Google Duplex. With so much information gathered from these exchanges, further research and development on Natural Language Processing may be done, and it can be used in a number of sectors, such as business, technology, and healthcare. Sentiment analysis may be used, for example, in the healthcare industry to diagnose patients and build diagnostic models for early diagnosis of chronic disease. Sentiment analysis handles these large datasets more rapidly and efficiently with the use of Natural Language Industries. Chatbots, which have uses in customer service, healthcare, education, and workplace assistance, are becoming more important entry points to digital services and information. On the other hand, not much is known about how chatbots affect people individually, in groups, or in society as a whole. Moreover, a number of problems need to be resolved before chatbots can reach their full potential. As a result, in recent years, chatbots have become a prominent area of research. We propose a research agenda that outlines future objectives and challenges for chatbot research in order to further knowledge in this rapidly expanding field of study. This proposal synthesizes years of chatbot research debate at the CONVERSATIONS workshop series. utilizing a collaborative approach for study analysis among. Sentiment analysis is a technique or procedure used to identify and extract certain subjects from spoken and written language, such as beliefs and attitudes. Sentiment analysis, broadly speaking, is the capacity to evaluate the sentiment of a subject and categorize the general polarity of the topic phrase as positive, negative, or neutral (Kang & Park, 2014). Over the past ten years, sentiment analysis has gained significant scholarly attention.

APRENDIZAJE AUTOMÁTICO PARA EL DIAGNÓSTICO MÉDICO PREDICTIVO, APLICACIÓN EN ZONAS INDUSTRIALES

Environmental pollution is a risk factor for chronic diseases (CD), which today are identified as the main cause of death in the world, 80% in low- and middle-income countries, in people of all ages. Given that industrial areas maintain a high rate of air pollution, their inhabitants are considered highly vulnerable, such is the case of the Metropolitan Area of Tula Hgo., in Mexico. At the request of the World Health Organization to integrate vulnerable populations to the quality of life through innovative strategies, the present study aims to build prediction models for CD of higher frequency in the area, to support predictive diagnosis through machine learning algorithms, recognized for their high performance in health areas. Based on the CRISP-DM methodology, requirements, characteristics and data behavior were analyzed, exhaustive cleaning and minmax scaler normalization were performed, the models were trained and validated with 80% - 20% of records, dropout and early stopping were applied to combat overtraining. The comparative analysis between 9 built models demonstrated the best performance of 3 of them, one for each EC; the Artificial Neural Network (ANN) for respiratory diseases and Random Forest (RF) for diabetes and high blood pressure. Its results of accuracy, precision, sensitivity, specificity and F1-score were 99%, 99%, 100%, 99% and 99.49% respectively for ANN, the RF model for diabetes obtained 98%, 100%, 97%, 100% and 98.7% and for arterial hypertension 95%, 97%, 94, 97% and 95.47%, these models were integrated into a graphical interface. The proposal constitutes a high-precision technological strategy for prevention and early diagnosis of CD in industrial areas, aimed at reducing mortality and improving the quality of life of the inhabitants.

PREDICTING CHRONIC DISEASES IN HIGHLY POLLUTED INDUSTRIAL ENVIRONMENTS USING MACHINE LEARNING

ABSTRACT: Environmental pollution is a risk factor for chronic diseases (CD), which today are identified as the main cause of death in the world, 80% in low- and middle-income countries, in people of all ages. Given that industrial areas maintain a high rate of air pollution, their inhabitants are considered highly vulnerable, such is the case of the Metropolitan Area of Tula Hgo., in Mexico. At the request of the World Health Organization to integrate vulnerable populations to the quality of life through innovative strategies, the present study aims to build prediction models for CD of higher frequency in the area, to support predictive diagnosis through machine learning algorithms, recognized for their high performance in health areas. Based on the CRISP-DM methodology, requirements, characteristics and data behavior were analyzed, exhaustive cleaning and minmax scaler normalization were performed, the models were trained and validated with 80% - 20% of records, dropout and early stopping were applied to combat overtraining. The comparative analysis between 9 built models demonstrated the best performance of 3 of them, one for each EC; the Artificial Neural Network (ANN) for respiratory diseases and Random Forest (RF) for diabetes and high blood pressure. Its results of accuracy, precision, sensitivity, specificity and F1-score were 99%, 99%, 100%, 99% and 99.49% respectively for ANN, the RF model for diabetes obtained 98%, 100%, 97%, 100% and 98.7% and for arterial hypertension 95%, 97%, 94, 97% and 95.47%, these models were integrated into a graphical interface. The proposal constitutes a high-precision technological strategy for prevention and early diagnosis of CD in industrial areas, aimed at reducing mortality and improving the quality of life of the inhabitants.

Microfluidic integrated gas sensors for smart analyte detection: a comprehensive review.

The utilization of gas sensors has the potential to enhance worker safety, mitigate environmental issues, and enable early diagnosis of chronic diseases. However, traditional sensors designed for such applications are often bulky, expensive, difficult to operate, and require large sample volumes. By employing microfluidic technology to miniaturize gas sensors, we can address these challenges and usher in a new era of gas sensors suitable for point-of-care and point-of-use applications. In this review paper, we systematically categorize microfluidic gas sensors according to their applications in safety, biomedical, and environmental contexts. Furthermore, we delve into the integration of various types of gas sensors, such as optical, chemical, and physical sensors, within microfluidic platforms, highlighting the resultant enhancements in performance within these domains.

IoMT Meets Machine Learning: From Edge to Cloud Chronic Diseases Diagnosis System.

In conventional healthcare, real-time monitoring of patient records and information mining for timely diagnosis of chronic diseases under certain health conditions is a crucial process. Chronic diseases, if not diagnosed in time, may result in patients' death. In modern medical and healthcare systems, Internet of Things (IoT) driven ecosystems use autonomous sensors to sense and track patients' medical conditions and suggest appropriate actions. In this paper, a novel IoT and machine learning (ML)-based hybrid approach is proposed that considers multiple perspectives for early detection and monitoring of 6 different chronic diseases such as COVID-19, pneumonia, diabetes, heart disease, brain tumor, and Alzheimer's. The results from multiple ML models are compared for accuracy, precision, recall, F1 score, and area under the curve (AUC) as a performance measure. The proposed approach is validated in the cloud-based environment using benchmark and real-world datasets. The statistical analyses on the datasets using ANOVA tests show that the accuracy results of different classifiers are significantly different. This will help the healthcare sector and doctors in the early diagnosis of chronic diseases.

A flexible and highly sensitive organic electrochemical transistor-based biosensor for continuous and wireless nitric oxide detection

As nitric oxide (NO) plays significant roles in a variety of physiological processes, the capability for real-time and accurate detection of NO in live organisms is in great demand. Traditional assessments of NO rely on indirect colorimetric techniques or electrochemical sensors that often comprise rigid constituent materials and can hardly satisfy sensitivity and spatial resolution simultaneously. Here, we report a flexible and highly sensitive biosensor based on organic electrochemical transistors (OECTs) capable of continuous and wireless detection of NO in biological systems. By modifying the geometry of the active channel and the gate electrodes of OECTs, devices achieve optimum signal amplification of NO. The sensor exhibits a low response limit, a wide linear range, high sensitivity, and excellent selectivity, with a miniaturized active sensing region compared with a conventional electrochemical sensor. The device demonstrates continuous detection of the nanomolar range of NO in cultured cells for hours without significant signal drift. Real-time and wireless measurement of NO is accomplished for 8 d in the articular cavity of New Zealand White rabbits with anterior cruciate ligament (ACL) rupture injuries. The observed high level of NO is associated with the onset of osteoarthritis (OA) at the later stage. The proposed device platform could provide critical information for the early diagnosis of chronic diseases and timely medical intervention to optimize therapeutic efficacy.

A novel early diagnostic framework for chronic diseases with class imbalance

Chronic diseases are one of the most severe health issues in the world, due to their terrible clinical presentations such as long onset cycle, insidious symptoms, and various complications. Recently, machine learning has become a promising technique to assist the early diagnosis of chronic diseases. However, existing works ignore the problems of feature hiding and imbalanced class distribution in chronic disease datasets. In this paper, we present a universal and efficient diagnostic framework to alleviate the above two problems for diagnosing chronic diseases timely and accurately. Specifically, we first propose a network-limited polynomial neural network (NLPNN) algorithm to efficiently capture high-level features hidden in chronic disease datasets, which is data augmentation in terms of its feature space and can also avoid over-fitting. Then, to alleviate the class imbalance problem, we further propose an attention-empowered NLPNN algorithm to improve the diagnostic accuracy for sick cases, which is also data augmentation in terms of its sample space. We evaluate the proposed framework on nine public and two real chronic disease datasets (partly with class imbalance). Extensive experiment results demonstrate that the proposed diagnostic algorithms outperform state-of-the-art machine learning algorithms, and can achieve superior performances in terms of accuracy, recall, F1, and G_mean. The proposed framework can help to diagnose chronic diseases timely and accurately at an early stage.

Diagnosis of Chronic Diseases Based on Patients’ Health Records in IoT Healthcare Using the Recommender System

Due to the growth of IoT applications, especially health care, the information of patients’ health records using data collection from IoT-connected devices has been considered. Biological data of patients in the health record helps to monitor the patient’s status and identify various diseases. Chronic diseases are a type of silent disease that, if not diagnosed in time, can cause irreparable damage to patients. The use of patients’ medical record data for early diagnosis of chronic diseases has recently attracted the attention of many researchers. On the other hand, the application of machine learning methods in the form of recommender systems has taken an important step in improving medical services and health care. In this paper, a medical recommender system was presented to identify and treat chronic diseases using an IoT device. In the present method, the electronic patient health record dataset that is loaded in the PhysioNet data repository has been used. In the present dataset, patients’ health records have been recorded according to the identified diseases and the physician’s diagnosis. In the proposed method, the K -nearest neighbor classification method is used to identify the type of disease, and the collaborative filtering method is used to find the appropriate treatment for patients. The results of the implementation of the proposed method show that this approach, based on the use of symptom similarity among patients, has good accuracy in diagnosing and predicting chronic diseases and has provided higher results than previous methods.

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction.

Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.

La dimissione protetta del bambino clinicamente complesso

Background - In the last few years care for children with severe chronic conditions is a topic of increasing interest. Children with chronic illness often require a high complexity of care and a high level of coordination among health services. Objective - The primary aim of this study is the assessment of children with chronic disease and high complexity of care (children with medical complexity), discharged from the Neonatal Intensive Care Unit (NICU) of Modena University-Hospital. The secondary aim is to assess the outcome of these patients. Methods - Patients with chronic diseases and high complexity of care (children with medical complexity), aged 0-6 months, and admitted to the NICU of Modena in the years 2009-2019 were recruited. Results - 59 subjects were included, of which 15 (25.4%) were born preterm, 11 (18.6%) suffered from hypoxic-ischemic encephalopathy, 33 (56%) had genetic diseases or malformations. All patients were discharged home, except for one patient who was discharged to hospice. The average hospital length of stay was 116.0 ± 63.4 days. Follow-up visits showed that 25 cases (42.4%) remained unchanged, 18 (30.5%) improved and 16 (27.1%) died. Conclusion - This study allows an assessment of patients with early diagnosis of chronic disease with high complexity of care, showing that some of these patients could be eligible for palliative care. The management of children with medical complexity requires diagnostic-therapeutic programmes that focus on them and their families and connect hospital and territorial health services.

Review of Feature Selection, Dimensionality Reduction and Classification for Chronic Disease Diagnosis

The early diagnosis of chronic diseases plays a vital role in the field of healthcare communities and biomedical, where it is necessary for detecting the disease at an initial phase to reduce the death rate. This paper investigates the use of feature selection, dimensionality reduction and classification techniques to predict and diagnose chronic disease. The appropriate selection of attributes plays a crucial role in improving the classification accuracy of the diagnosis systems. Additionally, dimensionality reduction techniques effectively improve the overall performance of the machine learning algorithms. On chronic disease databases, the classification techniques deliver efficient predictive results by developing intelligent, adaptive and automated system. Parallel and adaptive classification techniques are also analyzed in chronic disease diagnosis which is used to stimulate the classification procedure and to improve the computational cost and time. This survey article represents the overview of feature selection, dimensionality reduction and classification techniques and their inherent benefits and drawbacks.

Tb3+-atorvastatin doped in poly(ethylene glycol) optical biosensor for selective determination of progesterone and testosterone in serum samples.

An innovative, simple and cost effective Tb3+–atorvastatin photo probe was designed and used as a core for a spectrofluorometric approach to sensitively determine two vital biological compounds in serum samples. Tb3+–atorvastatin complex displays a characteristic electrical band with λem at 545 nm with significant luminescence intensity, which is quenched in the presence of progesterone and testosterone at two variant sets of pH; 6.2 and 7.5, respectively. The conditions were optimized and the best solvent for operation was found to be acetonitrile with λex at 320 nm. Progesterone and testosterone were assessed in serum samples using the same optimal conditions within concentration ranges of 2 × 10−9 to 2.9 × 10−6 and 3.1 × 10−9 to 4.8 × 10−6 mol L−1, respectively. The proposed luminescence method was validated in accordance to ICH guidelines and found to be accurate, precise and specific and free from any interference. The cost effectiveness and applicability of the method make it a good choice for routine analysis of the two compounds and early diagnosis of chronic diseases associated with abnormalities in their physiological levels.

PH assists simultaneous determination of folic acid and vitamin D3 in biological fluids using a novel Tb3+-acyclovir optical biosensor.

An innovative, simple and cost effective Tb3+–acyclovir photo probe was designed and used as a core for a spectrofluorometric approach to sensitively determine two vital biological compounds in different matrices. The Tb3+–acyclovir complex displays a characteristic electrical band with λem at 545 nm with significant luminescence intensity, which is quenched in the presence of folic acid and vitamin D3 at pH 5.0 and 9.0, respectively. The conditions were optimized and the best solvent for operation was found to be acetonitrile and λex at 340 nm. folic acid was successfully estimated in tablet dosage form, urine and serum in the concentration range of 2.28 × 10−6 to 1.49 × 10−9 mol L−1. Vitamin D3 was also assessed in serum samples using the same optimal conditions within the concentration range of 3.2 × 10−9 to 1.0 × 10−6 mol L −1. The proposed luminescence method was validated in accordance with ICH guidelines and found to be accurate, precise and specific and free from any interferences. The cost effectiveness and applicability of the method make it a good choice for routine analysis of the two compounds and early diagnosis of chronic diseases associated with abnormalities in their physiological levels.

New Tb3+-simvastatin optical biosensor for sensitive determination of folic acid, progesterone, testosterone and vitamin D3 in biological fluids.

An innovative, simple and cost effective Tb3+–simvastatin photo probe was designed and used as a core for a spectrofluorometric approach to sensitively determine four vital biological compounds in different matrices. A Tb3+–simvastatin complex displays a characteristic electrical band with λem at 545 nm with significant luminescence intensity, which is quenched in the presence of folic acid, progesterone, testosterone and vitamin D3 at four variant sets of pH: 5.0, 6.2, 7.5 and 9.0, respectively. The conditions were optimized and the best solvent for operation was found to be acetonitrile at λex at 340 nm. Folic acid was successfully estimated in tablet dosage form, urine and serum in the concentration range of 2.49 × 10−9 to 1.28 × 10−6 mol L−1. Progesterone, testosterone and vitamin D3 were also assessed in serum samples using the same optimal conditions within concentration ranges of 5 × 10−9 to 1.9 × 10−6, 5 × 10−9 to 2.8 × 10−6 and 5 × 10−9 to 4.2 × 10−6 mol L−1, respectively. The proposed luminescence method was validated in accordance with ICH guidelines and found to be accurate, precise, and specific and free from any interference at the working pH for each analyte. The cost effectiveness and applicability of the method make it a good choice for routine analysis of the four compounds and early diagnosis of chronic diseases associated with abnormalities in their physiological levels.

AI's Legitimate Interest

Health data uses are on the rise. Increasingly more often, data are used for a variety of operational, diagnostic, and technical uses, as in the Internet of Health Things. Never has quality data been more necessary: large data stores now power the most advanced artificial intelligence applications, applications that may enable early diagnosis of chronic diseases and enable personalized medical treatment. These data, both personally identifiable and de-identified, have the potential to dramatically improve the quality, effectiveness, and safety of artificial intelligence. Existing privacy laws do not 1) effectively protect the privacy interests of individuals and 2) provide the flexibility needed to support artificial intelligence applications. This paper identifies some of the key challenges with existing privacy laws, including the ineffectiveness of de-identification and data minimization protocols in practice and issues with notice and consent as they apply to artificial intelligence applications, then proposes an alternative privacy model. This model introduces a more restrictive approach to health privacy while introducing an interest balancing approach to data processing and retention to benefit individuals and the general public.

Feature Selection Using Improved Teaching Learning Based Algorithm on Chronic Kidney Disease Dataset

Abstract Feature selection plays an important role in almost any data mining application especially in medical data mining to solve the problem of ‘curse of dimensionality’ and provide early diagnosis with relevant features and high accuracy. Innumerable feature selection methods have been presented in state-of-arts literature to tackle the problems of high dimensional data. Many evolutionary and swarm intelligence algorithms find solutions based on algorithm-specific control parameters. However, it is a challenging task to identify the optimal feature subset using a feature selection algorithm that is not dependent on the controlling parameters of an algorithm that is specific to a particular problem in hand. Hence, the present research work is based on the working principle of the original TLBO algorithm which does not require any algorithm-specific parameters. The proposed research work is known as Improved Teacher Learner Based Optimization (ITLBO) algorithm which aims to select the best feature subset based on Chebyshev distance formula in the evaluation of the fitness function and common control parameters viz., population size and number of generations to find the optimal feature subset for early diagnosis of chronic diseases. The proposed feature selection technique was applied to Chronic Kidney Disease (CKD) dataset and has achieved a significant feature reduction of 36% compared to the feature reduction of 25 % obtained by applying the original TLBO algorithm. The derived optimal feature subset obtained from TLBO algorithm and feature subset obtained from ITLBO algorithm is validated by evaluating the accuracy of Support Vector Machine (SVM), Convolution Neural Networks (CNN) and Gradient Boosting classification algorithms. Experimental results reveal that there is an overall improvement of classification accuracy for the three algorithms for the derived feature subset from the proposed feature selection algorithm compared to the original TLBO algorithm.

Can decreased heart rate variability be a marker of autonomic dysfunction, metabolic syndrome and diabetes?

Epidemiological studies show an emergence of diet- and lifestyle-related diseases; Cardio-metabolic diseases (CMD) and neuropsychiatric diseases (classified as non-communicable diseases or chronic diseases). Diet and lifestyle factors can cause adverse effects on autonomic function resulting in decreased heart rate variability (HRV). Low HRV is a risk factor for CMDs. There is a need to find out new methods of early diagnosis for prevention and treatment of these problems because the neurohormonal dysfunction could be the earliest manifestation. It is possible that HRV could be a marker for the early diagnosis of these problems, because it is characterised with increased sympathetic and reduced parasympathetic activity. Several studies indicate that increased unhealthy diet, mental stress, sedentary lifestyle, tobacco, insomnia and alcoholism may be associated with neurohormonal dysfunction, which may cause decline in HRV. Majority of the chronic diseases (e.g., diabetes, hypertension, heart attack, neuropsychiatric disease and cancer) are associated with decreased HRV. The studies also indicate that solar and geomagnetic activities may influence circadian clock and hypothalamus resulting in the oxidative stress and inflammation with alteration in HRV. It is possible that reduced HRV will correlate with various stages of autonomic dysfunction, associated with chronic diseases. Simple methods need to be developed to measure HRV for early diagnosis of neurohormonal dysfunction, which may be important for early management. This review aims to find out available evidence on the role of HRV in the early diagnosis of chronic disease (with specific focus on Type 2 diabetes) and the factors affecting HRV.

Electrical performance of PEDOT:PSS-based textile electrodes for wearable ECG monitoring: a comparative study

BackgroundWearable textile electrodes for the detection of biopotentials are a promising tool for the monitoring and early diagnosis of chronic diseases. We present a comparative study of the electrical characteristics of four textile electrodes manufactured from common fabrics treated with a conductive polymer, a commercial fabric, and disposable Ag/AgCl electrodes. These characteristics will allow identifying the performance of the materials when used as ECG electrodes. The electrodes were subjected to different electrical tests, and complemented with conductivity calculations and microscopic images to determine their feasibility in the detection of ECG signals.MethodsWe evaluated four electrical characteristics: contact impedance, electrode polarization, noise, and long-term performance. We analyzed PEDOT:PSS treated fabrics based on cotton, cotton–polyester, lycra and polyester; also a commercial fabric made of silver-plated nylon Shielde® Med-Tex P130, and commercial Ag/AgCl electrodes. We calculated conductivity from the surface resistance and, analyzed their surface at a microscopic level. Rwizard was used in the statistical analysis.ResultsThe results showed that textile electrodes treated with PEDOT:PSS are suitable for the detection of ECG signals. The error detecting features of the ECG signal was lower than 2% and the electrodes kept working properly after 36 h of continuous use. Even though the contact impedance and the polarization level in textile electrodes were greater than in commercial electrodes, these parameters did not affect the acquisition of the ECG signals. Fabrics conductivity calculations were consistent to the contact impedance.