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All Journal International Journal of Advances in Applied Sciences Folia Medica Indonesiana
Upreti, Kamal
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Biochemistry, Genetics & Molecular Biology Earth & Planetary Sciences Environmental Science Health Professions Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics

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Discovering Patterns of Cardiovascular Disease and Diabetes in Myocardial Infarction Patients Using Association Rule Mining Singh, Anju; Singh, Divakar; Sharma, Shikha; Upreti, Kamal; Maheshwari, Manish; Mehta, Vimal; Sharma, Jitender; Mehra, Pratishtha; Dabla, Pradeep Kumar
Folia Medica Indonesiana Vol. 58, No. 3
Publisher : Folia Medica Indonesiana

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Abstract

Highlights: • Association Rule Mining tools predict the association of early-onset Myocardial Infarction with Hypertension and Diabetes Mellitus. • Association Rule Mining tools using clinical and biochemical attributes can predict the development of Hypertension and Diabetes Mellitus in Myocardial Infarction patients. Abstract: Cardiovascular diseases (CVDs) are a major cause of mortality in diabetic patients. Hypertensive patients are more likely to develop diabetes and hypertension contributes to the high prevalence of CVDs, in addition to dyslipidemia and smoking. This study was to find the different patterns and overall rules among CVD patients, including rules broken down by age, sex, cholesterol and triglyceride levels, smoking habits, myocardial infarction (MI) type on ECG, diabetes, and hypertension. The cross-sectional study was performed on 240 subjects (135 patients of ST-elevation MI below 45 years and 105 age matched controls). Association rule mining was used to detect new patterns for early-onset myocardial infarction. A hotspot algorithm was used to extract frequent patterns and various promising rules within real medical data. The experiment was carried out using "Weka'', a tool for extracting rules to find out the association between different stored real parameters. In this study, we found out various rules of hypertension like "Rule 6” says that if levels of BP Systolic > 131 mmHg, LpA2 > 43.2 ng/ml, hsCRP > 3.71 mg/L, initial creatinine > 0.5 mg/dl, and initial Hb ≤15 g/dl (antecedent), then the patient will have 88% chance of developing hypertension (consequent). Similarly for diabetes mellitus with finding their lift and confidence for different support like "Rule 6”, if MI type on ECG = 'Inferior Wall MI' with STATIN=No, and levels of Triglycerides ≤325 (antecedent), then the patient had a 67% chance of developing diabetes mellitus. We concluded that early-onset myocardial infarction is significantly associated with hypertension and diabetes mellitus.Using association rule mining, we can predict the development of hypertension and diabetes mellitus in MI patients.
AI-driven emotion recognition systems for sustainable mental health care: an engineering perspective Ahmad, Akram; Singh, Vaishali; Upreti, Kamal
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1111-1117

Abstract

Emotion recognition systems are transforming human-computer interaction (HCI) applications by enabling AI-driven, adaptive, and responsive mental health interventions. This study explores AI-based emotion recognition technologies using facial expressions, voice analysis, text-based sentiment processing, and physiological signals to develop scalable, real-time mental health support systems. Utilizing datasets such as FER2013, JAFFE, and CK+, our research examines deep learning models, including EfficientNet XGBoost, which achieved over 90% accuracy across key evaluation metrics. Unlike traditional mental health interventions, AI-driven systems provide cost-effective, accessible, and sustainable solutions through telemedicine, wearable biosensors, and virtual counselors. The study also highlights critical challenges such as algorithmic bias, ethical AI compliance, and the energy consumption of deep learning models. By integrating machine learning, cloud-based deployment, and edge computing, this research contributes to the development of sustainable, ethical, and user-centric AI solutions for mental health care. Future directions include AI model optimization for energy-efficient deployments and the creation of diverse, inclusive datasets to improve performance across global populations.
Co-Authors Ahmad, Akram Maheshwari, Manish Mehra, Pratishtha Mehta, Vimal Pradeep Kumar Dabla, Pradeep Kumar Sharma, Jitender Shikha Sharma, Shikha Singh, Anju Singh, Divakar Singh, Vaishali