International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056 p-ISSN: 2395-0072
DIAGNOSIS OF ACUTE DISEASES IN VILLAGES AND SMALLER TOWNS USING AI Chandrashekhar K S 1, Praveen P 2, Bhuvaneshwar C 3, Manasa H A 4, Radhika Sreedharan 5 1234 UG student, Dept. of Computer Science & Technology, Presidency University, Bengaluru. 5 Assistant Professor, Dept. of Computer Science & Engineering, Presidency University, Bengaluru.
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Abstract - The rapid growth in AI-driven healthcare solutions has paved the way for advanced diagnostic tools, especially in resource-constrained environments like villages and smaller towns. Acute diseases often require timely intervention, and delays in diagnosis can have severe health consequences. This project addresses these challenges by developing an AI-based system capable of diagnosing acute diseases in under-served areas. The system leverages machine learning models trained on diverse medical datasets and offers a cost-effective, scalable solution to support healthcare providers in rural areas. The development process of this AI-based diagnostic system involves several key phases, including data collection, preprocessing, model training, system integration, and performance evaluation. A large dataset comprising patient records and disease-related data has been used to train and validate the models. This project aims to bridge the healthcare accessibility gap in underdeveloped areas, providing timely, accurate, and cost-effective diagnostic support. The AI-driven system offers a scalable solution that can be extended to diagnose a wider range of diseases in the future. By leveraging AI, the system empowers healthcare workers and reduces the burden on overstretched medical resources, ultimately leading to better health outcomes for rural populations. Key Words: Artificial Intelligence (AI) in Healthcare, Acute Disease Diagnosis, AI for Rural Healthcare ,Machine Learning in Disease Prediction, Natural Language Processing (NLP) for Symptom Analysis, Deep Learning in Medical Imaging, Predictive Analytics in Health, AI-based Recommendation Systems ,Symptom Mapping and Feature Engineering ,AI in Resource-Constrained Environments, Healthcare Accessibility in Rural Areas, AIDriven Healthcare Innovations ,Explainability in AI Models, Real-Time Disease Diagnosis, AI for Healthcare Workers Support ,AI and Data Privacy in Healthcare ,Scalable Healthcare Solutions, Modular System Design in AI ,Disease Prediction Accuracy, Community Health Monitoring with AI.
solutions to some of the biggest and challenging problems global. Every industry that rely on lot of amount of data, from medicinal, finance, energy, logistical right the way to even car manufacturing has benefited from the use of AI for its data processing, pattern recognition and predicting abilities.
1.1 The Role of AI in Solving Complex Challenges In light of this, with the steady increase in sizes and sophistication of datasets that is readily available, Machine learning models have never failed to amaze every solver of challenges that one could think could not be solved. Traditionally uses of inpaints such as pattern recognition, predictive analytics, anomaly detection, optimization, and the likes have since improved through the incorporation of AI. But to successfully apply these technologies in solving problems within a given domain, one has to get to a refined method. For instance: In the context of healthcare, AI models are now applied for the purpose of augmenting the accuracy of the current disease diagnosis, assessing patient prognosis and for defining selecting customized treatment plans. SDKs still are an issue for rural and underprivileged locations, leaving poor diagnosis as an area where AI can excel.
Fig. 1.1 Overview of Artificial Intelligence
1. INTRODUCTION AI has established itself as one of the key drivers of transformation in the 21st century and is reshaping industries by providing new and efficient and data-driven
Impact Factor value: 8.315
The project's objectives focus on multiple aspects. First, a robust data preprocessing pipeline will eliminate inefficiencies by cleaning datasets and applying normalization techniques for optimal model performance.
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