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193 articles for “disease prediction”
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Literature Review and Discussion of Machine Learning Algorithms for Predicting Chronic Kidney Disease
Abstract: Being one of the most serious and most occurring diseases in our era, chronic kidney disease requires a fast and correct diagnosis. The usage of machine learning in medicine has now grown to such a level that it could be a means of diagnosis. The doctor can be the first one to get the ailment by using machine learning classifier algorithms. This has been the data science sector’s new horizons, …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 34–39 Read article
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Statistical Models for Predicting Genetic Variability and Disease Susceptibility
Abstract: Differences in genetics are key to understanding why some individuals are more prone to certain diseases than others. Recent advancements in genomic research, combined with statistical modeling techniques, have made significant strides in predicting disease risk based on genetic factors. This review explores the application of statistical models for predicting genetic variability and their role in disease susceptibility. We discuss traditional methods like linear regression and genome-wide association studies (GWAS), …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 30–34 Read article
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Design and Implementation of a Smart Healthcare Communication Bot
Abstract: Healthcare has proved to be one of the core factors that lead us to a magnificent life. Moreover, going to a professional for every small health issue is complicated and difficult task. To aid users with health issues, this paper describes the creation and deployment of a multifunctional healthcare assistance application. The application encompasses: The first task of the application involves a chatbot interface that gives in- depth drug information …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 2, Issue 1, 2024 · pp. 14–20 Read article
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Next Generation Sequencing (NGS) in Personalized Medicine: Emphasizing Public Health Beyond Reality
Abstract: NGS is a modern technology that allows researchers to quickly sequence large amounts of DNA or RNA, it is also known as massively parallel sequencing or deep sequencing. NGS has revolutionized genomic research and biological sciences by its speed, scale, accuracy, ultra-high throughput, and cost. NGS replaced the conventional sequencing method. A novel idea in healthcare is precision medicine, which involves adjusting medical care to each patient’s unique traits and …
Published in Recent Trends in Infectious Diseases · Vol. 2, Issue 1, 2025 · pp. 1–17 Read article
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A Comprehensive Review of DiagnoTech: Your Healthcare Companion
Abstract: The review paper “A comprehensive review of DiagnoTech: Your Healthcare Companion” brings forth a transformative solution in the advancements of healthcare technology which aims to be an all-in-one Healthcare Companion. It explores the four key elements of DiagnoTech: Lab Tests, Body Mass Index (BMI) Calculation, Yoga and Diet Recommendations, and disease prediction. It brings to light each of these features’ functionalities, integration, and their collective impact on the healthcare industry. …
Published in International Journal of Mobile Computing Technology · Vol. 2, Issue 2, 2024 · pp. 24–32 Read article
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NutriHeart with Chatbot
Abstract: Heart disease stands as one of the world's principal reasons for human deaths since it causes major preventable fatalities each year. Healthcare institutions currently explore machine learning (ML) integration for establishing new approaches toward predicting, and acting ahead of healthcare developments. NutriHeart presents an AI-based platform that accomplishes cardiovascular risk detection early and extends heart wellness by delivering customized nutritional and lifestyle recommendations. Using Support Vector Machines (SVM) along with …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 3, 2025 · pp. 22–34 Read article
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Virtual Method to Predict Dental Disease
Abstract: The integration of technology and medicine in the healthcare domain has led to the emergence of inventive strategies to improve patient care and diagnostics. One such groundbreaking methodology is the utilization of Convolutional Neural Networks (CNNs) within the domain of deep learning, particularly for image recognition and processing tasks. In this paper, we propose a novel approach to image recognition that employs state-of-the-art deep learning algorithms to create a user-friendly …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 8–15 Read article
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Farmer’s Pal
Abstract: Precision agriculture, characterized by data-driven decision-making, has transformed contemporary farming practices. To increase agricultural sustainability and efficiency, this abstract investigates the combination of sensor monitoring, machine learning, and picture processing. A network of sensors continuously collects vital environmental data, including temperature, humidity, rainfall, sunshine, soil moisture, and conductivity, for precision agriculture. By providing real-time insights, these sensors enable farmers to make informed choices about pest control, fertilization, and irrigation. This …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 2, 2025 · pp. 19–31 Read article
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Transforming Rare Disease Diagnosis with AI
Abstract: Artificial intelligence is changing healthcare fast. It is making diagnoses accurate, helping doctors get better results, and streamlining how care works. This paper looks at how AI shows up in healthcare right now – where it is already making a difference, what is working, and what is still tricky. The focus is on machine learning, natural language processing, and computer vision. Particular attention is given to using AI in diagnosing …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 Read article
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Explainable Machine Learning Integrated with Polymer-Based Diagnostic Technologies for Liver Health Classification
Abstract: Early and reliable assessment of liver health is essential for timely treatment, yet most machine-learning approaches face limitations such as class imbalance and low clinical interpretability. This study proposes a polymer-integrated, explainable machine-learning framework that combines SMOTE-based data balancing, Logistic Regression, and XAI techniques (SHAP and LIME) for transparent liver-health classification. In addition to ML modelling, the study emphasizes the emerging role of polymer-based biosensors, microfluidic polymer chips, polymer nanomaterials, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 631–643 Read article
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MediSense AI - Smart Health Analysis System
Abstract: MediSense AI is a revolutionary health analysis system that empowers users by transforming complex medical data into understandable insights. This platform utilizes advanced technologies, particularly natural language processing and machine learning, to make intricate medical terminologies accessible to individuals without a healthcare background. Leveraging Llama 3, a cutting-edge AI model developed by Meta AI, the system can analyze various forms of medical data, including the ability for users to upload …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 3, 2024 · pp. 20–30 Read article
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Early Disease Detection Using Artificial Intelligence
Abstract: Growth in artificial intelligence and machine learning now make it possible for the healthcare sector to be totally transformed by a new chapter, particularly in the era of medical image analysis. This study focuses on harnessing these advancements to develop a sophisticated model for early disease detection across diverse medical domains, majorly in skin disease. By integrating diverse datasets and leveraging advanced algorithms, our methodology aims to identify subtle disease …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 3, 2024 · pp. 11–19 Read article
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Heart Disease AI-based Prediction: A Comparative Analysis
Abstract: The present investigation looks at how well various machine learning algorithms predict cardiac disease. Since heart disease is one of the major causes of death worldwide, early detection and precise diagnosis are essential for managing and treating the condition. Our goal is to enhance diagnostic processes and improve patient outcomes by leveraging machine learning techniques. Six widely-used machine learning algorithms are evaluated in this research paper. These algorithms were selected …
Published in Trends in Mechanical Engineering & Technology · Vol. 14, Issue 2, 2024 · pp. 21–29 Read article
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Nanotechnology-Enhanced Wearable Biosensors for Liver Disease Detection: Integration with AI for Predictive Analytics
Abstract: The worldwide health burden of liver diseases is substantial, and effective treatment and management depend heavily on early detection. This study investigates the integration of nanotechnology-enhanced wearable biosensors with artificial intelligence (AI) techniques for predictive analytics in liver disease detection. The construction of extremely selective and sensitive biosensors that can identify a variety of biomarkers linked to liver illnesses has been made possible via nanotechnology. These nanotechnology-based biosensors can be …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 14, Issue 1, 2024 · pp. 22–36 Read article
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Algorithm for the prediction of cardiovascular disease (CVD)
Abstract: cardiovascular diseases (CVD) still claim a significant number of deaths globally and remain the number one killer with an annual death toll of nearly 17.9 million. While several medical advancements have been made, an early diagnosis is still hard to obtain, which often leads to worsening conditions and intricate treatment options. With the advancement of modern technology, Machine learning has demonstrated to be a miraculous tool which can greatly impact …
Published in Research and Reviews : A Journal of Immunology · Vol. 15, Issue 2, 2025 Read article
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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article
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Artificial Intelligence in Microbiological Research: Methods, Applications and Implications
Abstract: Artificial Intelligence (AI) is revolutionising microbiological research by enabling the rapid analysis of complex biological data and improving the accuracy, efficiency, and reliability of scientific investigations. Recent advances in machine learning, deep learning, and bioinformatics have transformed AI into a powerful tool for studying microorganisms, their genetic composition, evolutionary patterns, and interactions with hosts and the environment. AI-driven computational models can process large and complex datasets far more efficiently than …
Published in Research and Reviews: A Journal of Microbiology and Virology · Vol. 16, Issue 2, 2026 Read article
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A Comprehensive Review on Federated Learning in Disease Detection
Abstract: Healthcare data, which is frequently dispersed among various organisations, has enormous potential to improve predictive analytics and illness identification. However, there are substantial privacy & legal obstacles to sharing this private data for centralised model training. Federated Learning is a paradigm shift that allows several organisations to work together to build a global model without disclosing raw patient information. Federated Learning uses a larger dataset to provide more reliable insights …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 1, 2026 · pp. 1–21 Read article
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AI-Powered ECG Prediction System for Detecting Cardiovascular Disease
Abstract: The proposed AI-powered CardioSmart Analyzer, an electrocardiogram (ECG) prediction system, presents an innovative and scientifically rigorous approach to the real-time automated analysis of ECG signals for diagnosing various heart conditions. This research focused on building a predictive model to identify cardiovascular diseases (CVD) using ECG data. A dataset comprising 2,840 12-lead ECG recordings was gathered from medical facilities in Gazipur, Bangladesh, over the period from June to August 2024. The …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 3, 2025 · pp. 51–85 Read article
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Advances in Biological Systems Modeling for Predicting Drug Effects in Chronic Disease
Abstract: Biological systems modeling has emerged as a promising tool for understanding and predicting the effects of drugs in the treatment of chronic diseases. Chronic diseases, such as diabetes, cardiovascular diseases, and neurodegenerative disorders pose significant challenges to traditional drug development due to their complex, multifactorial nature. Systems biology approaches, which integrate computational modeling with experimental data, provide a holistic view of disease mechanisms and treatment responses. This review explores recent …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 17–22 Read article