Artificial Intelligence in Investment Management, Asset Management and Warehouse Management
Influence of Digital Transformation and Artificial Intelligence in Business
A Study on Employee Perception towards Digital Marketing Services
A Study on Spending Patterns in the Digital Era with Special Reference to Tamilnadu
A Comparative Analysis for Identifying the Polarity of People Based on Emotional Pulse in a Smart City
A Comparative Analysis for Identifying the Polarity of People Based on Emotional Pulse in a Smart City
Video Analytics for Optimizing Bank Services
An Ensemble Technique to Predict Mental Illness using Data Mining Techniques
Tilime Crop Yield Prediction using Machine Learning Algorithms
Enhancing Donor Acquisition and Retention in Blood Banks via AI-Powered Decision Support Framework
This paper presents a comprehensive exploration into the utilization of machine learning (ML) techniques to revolutionize medical diagnostics, with a specific focus on enhancing the detection of heart disease. Recognizing the imperative need for early diagnosis to address the global prevalence of heart disease, this study delves into the development and application of advanced ML principles. The paper aims to construct a robust ML model capable of analyzing diverse patient data sets, including electronic health records and genetic information, to discern intricate patterns and correlations imperceptible to human clinicians. By leveraging a comprehensive dataset encompassing various patient profiles, the ML model is poised to significantly enhance the precision, speed, and efficiency of heart disease detection. The findings of this paper hold promise for fostering more effective intervention strategies and improving patient care outcomes in the realm of cardiovascular health.
Agriculture stands as the bedrock of Malawi's economy, involving nearly 90% of the population in subsistence farming. However, the sector faces challenges arising from unpredictable weather patterns, climate shifts, and environmental factors that threaten its sustainability. This paper proposes a pioneering solution leveraging Machine Learning (ML) to address these challenges, presenting a robust decision support system for Crop Yield Prediction (CYP). By harnessing ML capabilities, the system aids in crucial decisions related to crop selection and management throughout the growing season, specifically tailored for the unique agricultural landscape of Malawi. This approach aims to empower farmers by providing valuable insights into soil quality, composition, and nutrients, enabling informed decisions to maximize crop yield. Through the integration of advanced technology into the agricultural domain, this paper seeks to usher in a transformative era for Malawian agriculture, fostering resilience and sustainability in the face of evolving environmental dynamics.
The mental well-being of a person is their mental state. Chemical abnormalities in the brain cause mental health problems. It is important to monitor the mental health of different groups in order to predict health-related disorders. The community consists of working professionals and college students. It is widely believed that stress and grief affect people of all ages and backgrounds. Some serious mental health disorders, such as anxiety, bipolar disorder, and schizophrenia, often evolve and produce symptoms that can be recognized early. Such mental disorders could be avoided more successfully if abnormal mental states are detected in the early stages of the disease, allowing for additional care and treatment. This study analyzed the accuracy of four data mining techniques and introduced a new ensemble technique to improve their accuracy in identifying mental health issues. The data mining techniques are Logistic Regression, KNN Classifier, Decision Tree Classifier, and Random Forest. This paper provides scope for other researchers and practitioners seeking to achieve higher accuracy in identifying mental health issues using enhanced data mining algorithms to meet several accuracy criteria.
Cardiovascular diseases (CVDs) continue to be the world's leading cause of death. It is imperative that accurate risk assessment and early intervention be implemented. This study proposes a predictive modeling framework, termed "HeartGuard," designed to assess an individual's risk of developing cardiovascular disease. Leveraging a diverse dataset comprising demographic information, lifestyle factors, medical history, and biomarker data, advanced machine learning techniques are employed to construct robust predictive models. The developed models incorporate features such as age, gender, blood pressure, cholesterol levels, smoking status, physical activity, and family history to estimate the probability of CVD occurrence within a specified timeframe. The evaluation of the models using cross-validation and independent validation datasets demonstrates their high accuracy, sensitivity, and specificity. HeartGuard offers a reliable tool for clinicians to identify individuals at heightened risk of cardiovascular disease, enabling targeted preventive measures and personalized healthcare interventions to mitigate the burden of CVD morbidity and mortality.
This study evaluates the potential of machine learning algorithms for early-stage diabetes prediction. A dataset containing demographic information, medical history, and lab results was analyzed using Logistic Regression and Random Forest Classifier. The results showed that Random Forest algorithms were able to accurately predict diabetes at an early stage with high accuracy. The best-performing algorithm was found to be the Random Forest Classifier, with an accuracy of 98.0%. These findings suggest that machine-learning algorithms hold great promise for improving diabetes diagnosis and management. The results of this study provide valuable insights for future research in this area and may help inform the development of more effective and efficient screening and treatment strategies for diabetes.
