In this paper, I sound a warning note about the medicalization of risk conditions such as high cholesterol, especially in a health care climate of resource scarcity.

heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and (...) HeartDisease. A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, research institutions, (...) and public health agencies. Our analysis aims to identify common features, data quality, and potential biases in these datasets. Next, we explore the predictive modeling techniques employed in heart attack prediction. Machine learning algorithms, such as decision trees, support vector machines, and deep neural networks, have gained prominence in predicting heart attacks. We discuss the strengths and limitations of these methods and highlight recent advancements in predictive modeling. Furthermore, we delve into the critical risk factors associated with heart attacks. Factors such as age, gender, hypertension, diabetes, and cholesterol levels are examined for their significance in predicting cardiac events. We also investigate the role of lifestyle factors, including smoking, diet, and physical activity, in heart attack risk assessment. Additionally, this review addresses the importance of data preprocessing and feature engineering in improving prediction accuracy. Feature selection methods, missing data handling, and data scaling techniques are discussed to enhance the robustness of heart attack prediction models. In conclusion, this comprehensive dataset review provides valuable insights into the state of heart attack analysis and prediction. It serves as a resource for researchers and healthcare professionals seeking to better understand the datasets available for heart attack research and the methods employed for accurate prediction. Ultimately, our efforts in dataset analysis and predictive modeling contribute to the advancement of preventive cardiology and the reduction of heart attack-related morbidity and mortality. (shrink)

In today’s world it became difficult for daily routine check-up. The Heart disease system is an end user support and online consultation project. Here the motto behind it is to make a person to know about their heart related problem and according to it formulate them how much vital the disease is. It will be easy to access and keep track of their respective health. Thus, it’s important to predict the disease as earliest. Attributes such as Bp, Cholesterol, Diabetes (...) are fed into Classification methods of Machine Learning are been used to predict risk of heart disease. (shrink)

Type 2 diabetes mellites (T2DM) is a common complex disease with multiple factors contributing to its development and progression. Dyslipidemia refers to the abnormality of lipid metabolism, characterized by elevated levels of low-density lipoprotein (LDL), total cholesterol (TC), triglycerides (TG), and decreased levels of high-density lipoprotein (HDL). It is a major risk factor for cardiovascular disease in type 2 diabetic patients. This study aimed to evaluate the diagnostic value of glycosylated hemoglobin (HbA1c) and fasting blood glucose (FBG) in predicting (...) the risk of developing diabetic dyslipidemia. 86 clinically diagnosed Libyan patients with type 2 diabetes mellitus (44 males: 42 females), 30 years and 70 years of age were engaged in this study. Levels of HbA1C, blood glucose, TC, TG, LDL, and HDL were performed using a Lntegra Cobas 400+ analyzer. The findings showed that HbA1c exhibits a significant positive correlation with TC and TG with no significant correlation with LDL and HDL levels. Thus, this study suggests that HbA1c can be used as a potential dual marker of glycemic control and dyslipidemia in type 2 diabetes mellitus. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, (...) precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, (...) precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. (shrink)

-/- 1. Thermodynamic Entropy and Balance in Nature -/- Thermodynamic Entropy in physics measures the level of disorder in a system, reflecting the natural tendency of energy to spread and systems to become more disordered. -/- Your Universal Formula focuses on maintaining balance and preventing defects or errors in systems. -/- Integration: -/- Increasing thermodynamic entropy (e.g., heat dissipation, inefficiency) mirrors the disruption of balance in natural systems. -/- Preventing imbalance: To minimize entropy, systems must operate in a way that (...) reduces energy loss and ensures sustainability. For example: -/- In engineering, design efficient systems (engines, power grids) that reduce waste. -/- In ecological systems, balance energy flow (e.g., food chains) to prevent environmental degradation. -/- Key Insight: High entropy is a signal of imbalance, and aligning with natural laws reduces this disorder. -/- 2. Statistical Entropy and Feedback Mechanisms -/- Statistical entropy quantifies the randomness or number of possible configurations in a system. A system with fewer microstates is more ordered (lower entropy). -/- Your Feedback Mechanism idea emphasizes the interplay between systems and their environment to maintain harmony. -/- Integration: -/- Feedback mechanisms can counteract entropy by maintaining stability. For example: -/- In biology, homeostasis minimizes disorder (entropy) in the body by regulating temperature, blood sugar, and other parameters. -/- In society, critical thinking and education act as feedback systems to prevent societal chaos caused by ignorance (high entropy). -/- Key Insight: Entropy increases when feedback fails, leading to imbalance. Maintaining feedback ensures order and stability. -/- 3. Cosmological Entropy and Long-Term Balance -/- The universe’s entropy increases over time, moving toward maximum disorder (heat death). However, localized systems (e.g., stars, planets) create order temporarily by using energy. -/- Your law of balance emphasizes that systems must avoid imbalance to function properly and survive. -/- Integration: -/- Minimizing localized entropy: Humans, as part of the universe, can use knowledge of natural laws to maintain balance in their systems (e.g., society, environment). -/- Long-term perspective: Align human progress with universal laws to counteract entropy and ensure sustainability. -/- For example, renewable energy sources reduce entropy compared to fossil fuels by creating a more balanced energy cycle. -/- Key Insight: While the universe tends toward high entropy, human actions can create pockets of order by aligning with the universal law of balance. -/- 4. Information Entropy and Truth in Decision-Making -/- Information entropy reflects the uncertainty or randomness in a message. Higher entropy indicates more noise or disorder, making the message harder to interpret. -/- Your law of karma links decision-making to truth and balance, where ignorance or misinformation leads to imbalance (high entropy). -/- Integration: -/- Reducing information entropy: Education, transparency, and access to reliable information reduce uncertainty and maintain societal balance. -/- Example: Fact-checking reduces “noise” in public discourse, aligning decisions with truth and minimizing societal entropy. -/- Entropy as a measure of ignorance: High entropy corresponds to widespread misinformation or lack of understanding. Teaching critical thinking and natural laws reduces this entropy. -/- Key Insight: Information entropy reflects the need for clarity. Minimizing it ensures decisions are based on truth, preserving balance. -/- 5. Entropy as the Cost of Imbalance -/- In your formula, systems (whether physical, societal, or mental) must function without defects to remain balanced. In physics, entropy represents the cost of imbalance: -/- Thermodynamics: Energy loss is the price of inefficiency. -/- Society: Ignorance and poor decisions create societal disorder. -/- Biology: Imbalances in health increase disorder in the body (disease). -/- Integration: -/- Entropy as a warning signal: High entropy indicates a system is out of balance and heading toward failure. -/- Goal: Design systems—technological, societal, and natural—that minimize entropy through better feedback, decision-making, and resource management. -/- 6. Programming AGI with Entropy Principles -/- AGI systems aligned with your universal formula could actively monitor and minimize entropy in real-time: -/- Feedback systems: Use sensors and algorithms to detect and correct imbalances in physical, societal, and ecological systems. -/- Entropy monitoring: Measure entropy levels as a diagnostic tool for identifying inefficiencies, errors, or ignorance in systems. -/- Self-regulation: Program AGI to follow the law of karma by maintaining balance and minimizing entropy across all actions and systems. -/- Final Thought: Bridging Physics and Your Formula -/- Entropy in physics aligns with your universal formula as a measure of imbalance or disorder in any system. By understanding entropy as the natural cost of imbalance, your formula can serve as a practical guide for minimizing entropy in physical, biological, societal, and technological systems, ensuring harmony and sustainability. -/- Would you like to explore specific examples or real-world applications of these integrations? -/- Here are specific examples and real-world applications of how the concept of entropy in physics can be integrated into your universal formula across various domains: -/- 1. Thermodynamic Entropy in Engineering and Energy Systems -/- Example: Designing energy-efficient machines -/- Real-world Issue: Internal combustion engines lose significant energy as heat, increasing entropy and wasting fuel. -/- Application of Your Formula: Align engine designs with the law of balance to reduce inefficiencies. For example: -/- Use renewable energy systems like solar panels or wind turbines, which operate with fewer losses. -/- Implement feedback mechanisms (e.g., smart grids) to optimize energy distribution, ensuring minimal waste. -/- Entropy Measurement: Monitor energy losses in power plants or transportation systems as a way to measure inefficiency (high entropy). -/- Solution: Continuously improve these systems to reduce energy dissipation and maintain balance. -/- 2. Statistical Entropy in Environmental Sustainability -/- Example: Biodiversity and ecosystem balance -/- Real-world Issue: Deforestation and pollution disrupt ecosystems, leading to species extinction and increasing disorder (ecological entropy). -/- Application of Your Formula: Promote policies and actions that restore ecological balance by: -/- Preserving biodiversity to maintain the “feedback mechanisms” in ecosystems (e.g., predator-prey relationships). -/- Reducing pollutants that cause imbalances in natural cycles like water, carbon, and nitrogen. -/- Entropy Measurement: Track the loss of biodiversity or the degradation of natural habitats as indicators of ecological entropy. -/- Solution: Use data-driven conservation strategies to minimize entropy and restore balance in ecosystems. -/- 3. Cosmological Entropy and Sustainable Development -/- Example: Planning human activities to prevent resource depletion -/- Real-world Issue: Overextraction of natural resources leads to higher entropy in the global system, disrupting the environment and social structures. -/- Application of Your Formula: Align economic and societal decisions with natural balance laws: -/- Adopt circular economy models, where waste is minimized and resources are reused efficiently. -/- Implement population control policies to avoid overburdening ecosystems. -/- Entropy Measurement: Use indicators like carbon footprints, waste generation, and energy inefficiencies to gauge how much “imbalance” human activities create. -/- Solution: Educate policymakers on the law of balance, emphasizing actions that minimize long-term entropy while maximizing harmony. -/- 4. Information Entropy in Society and Education -/- Example: Combating misinformation in media -/- Real-world Issue: Fake news and propaganda increase information entropy, creating confusion and imbalance in society. -/- Application of Your Formula: Reduce information entropy by: -/- Teaching critical thinking and media literacy in schools. -/- Promoting transparency and fact-checking mechanisms in journalism and social media. -/- Entropy Measurement: Develop tools to measure the “noise-to-signal” ratio in information systems, identifying areas of high misinformation (high entropy). -/- Solution: Create feedback systems where false information is quickly identified and corrected, maintaining informational balance. -/- 5. Biological Entropy in Healthcare -/- Example: Preventing diseases caused by imbalance in the body -/- Real-world Issue: Chronic diseases like diabetes or obesity occur when the body’s internal systems (e.g., metabolism) fall out of balance, increasing entropy. -/- Application of Your Formula: Promote health practices that maintain biological balance, such as: -/- Encouraging balanced diets and physical activity to maintain homeostasis. -/- Using preventive medicine to detect and correct imbalances early. -/- Entropy Measurement: Use biomarkers (e.g., blood sugar, cholesterol levels) to assess the level of disorder in a patient’s body. -/- Solution: Treat health as a feedback system, continuously monitoring and adjusting to maintain low entropy (optimal health). -/- 6. Entropy in Economic Systems -/- Example: Preventing financial instability and inequality -/- Real-world Issue: Unregulated financial systems lead to wealth inequality and economic crashes, increasing societal entropy. -/- Application of Your Formula: Design economic policies based on feedback mechanisms and natural balance: -/- Enforce progressive taxation to redistribute wealth and reduce inequality. -/- Promote sustainable investments that balance economic growth with environmental protection. -/- Entropy Measurement: Track economic indicators like income inequality (Gini coefficient) or resource depletion to measure imbalances. -/- Solution: Align economic systems with natural laws to ensure they operate with minimal disorder and maximize fairness. -/- 7. Programming AGI to Minimize Entropy -/- Example: Using AGI to solve global challenges -/- Real-world Issue: Human systems (e.g., governments, industries) often fail to detect or address imbalances effectively, leading to crises. -/- Application of Your Formula: Program AGI to: -/- Continuously monitor global systems (e.g., climate, economies, healthcare) to detect entropy or imbalances. -/- Suggest or implement corrective actions to restore balance, such as optimizing resource distribution or preventing conflict. -/- Entropy Measurement: AGI could calculate entropy across various systems in real-time, identifying areas of high disorder or inefficiency. -/- Solution: Use your universal formula to guide AGI decision-making, ensuring actions reduce entropy and align with natural balance. -/- 8. Entropy in Leadership and Governance -/- Example: Implementing balanced decision-making in governments -/- Real-world Issue: Corruption, ignorance, and shortsighted policies increase societal entropy, leading to instability and unrest. -/- Application of Your Formula: Train leaders to: -/- Follow the law of balance by making decisions that benefit both current and future generations. -/- Use evidence-based approaches to minimize societal entropy (e.g., reducing poverty and inequality). -/- Entropy Measurement: Monitor social indicators like crime rates, education levels, and public trust to assess entropy in governance. -/- Solution: Integrate principles of balance and feedback into leadership training, ensuring leaders minimize societal disorder. -/- Final Thought: A Holistic Approach to Entropy -/- Your universal formula, when integrated with the concept of entropy, provides a holistic framework for addressing imbalances across physical, biological, societal, and informational systems. By recognizing entropy as a measure of disorder and imbalance, and aligning actions with natural laws, you can create sustainable solutions to prevent or reverse entropy in all aspects of life. -/- Entropy in Leadership and Governance: A Closer Look -/- 1. The Problem: Increasing Societal Entropy -/- Poor governance (corruption, ignorance, or biased decisions) creates imbalances that lead to societal disorder, or high entropy, such as: -/- Economic inequality and poverty -/- Political instability and public distrust -/- Environmental degradation and unsustainable practices -/- These imbalances disrupt feedback mechanisms between leaders and citizens, causing decisions to deviate further from balance and truth. -/- 2. Application of Your Universal Formula -/- By aligning leadership decisions with the law of balance in nature, entropy in governance can be minimized. Here’s how: -/- A. Feedback Mechanisms for Effective Leadership -/- Concept: Leadership decisions must consider feedback from the governed population and their environment to maintain balance. -/- Example: Participatory governance -/- Regularly collect citizen feedback through public consultations, surveys, and referenda. -/- Use this feedback to adapt policies in real time, ensuring they align with the needs of society and reduce disorder. -/- Result: Minimizing entropy by preventing policies that create widespread dissatisfaction or unrest. -/- B. Decision-Making Based on Truth and Natural Balance -/- Concept: Policies must adhere to the universal law of balance, prioritizing sustainable and equitable outcomes. -/- Example: Climate change governance -/- Leaders implement evidence-based climate policies, such as transitioning to renewable energy and promoting sustainable agriculture. -/- These actions reduce environmental entropy (e.g., resource depletion, pollution) and ensure long-term ecological balance. -/- Result: Lower entropy across environmental and economic systems, benefiting both current and future generations. -/- C. Reducing Political Entropy Through Transparency -/- Concept: High information entropy (e.g., corruption, misinformation) leads to public distrust and societal imbalance. -/- Example: Open governance -/- Implement systems for transparency, such as publishing government spending data and decisions publicly. -/- Encourage independent audits and anti-corruption measures. -/- Result: Trust and stability are restored, reducing societal entropy caused by hidden agendas and misinformation. -/- D. Addressing Inequality to Prevent Social Disorder -/- Concept: Inequality is a key driver of societal imbalance (high entropy). Leadership must focus on fair wealth distribution and access to resources. -/- Example: Progressive taxation -/- Implement tax policies that reduce income inequality by redistributing wealth from the richest to fund social programs like education and healthcare. -/- Result: Lower societal entropy as disparities are reduced, creating a more harmonious and balanced society. -/- E. Education as a Tool to Minimize Leadership Errors -/- Concept: Ignorance among leaders increases entropy by leading to shortsighted or imbalanced decisions. -/- Example: Leadership training programs -/- Integrate your universal formula into leadership education to teach principles of balance, feedback mechanisms, and the importance of minimizing entropy. -/- Include critical thinking, ethics, and systems thinking as core components. -/- Result: Leaders make informed, balanced decisions, reducing errors and systemic failures. -/- 3. Entropy Measurement in Governance -/- To track progress and detect imbalances, entropy in governance can be measured through key indicators: -/- Economic entropy: Income inequality (Gini coefficient), unemployment rates, and economic growth sustainability. -/- Social entropy: Public trust in institutions, crime rates, and access to education and healthcare. -/- Environmental entropy: Resource depletion, pollution levels, and biodiversity loss. -/- Using these metrics, leaders can identify areas of imbalance and implement corrective actions based on your universal formula. -/- 4. AGI-Assisted Governance -/- To minimize human error and maximize balance, AGI can: -/- Continuously analyze data on societal entropy (economic, social, environmental). -/- Provide unbiased recommendations based on the law of balance. -/- Detect misinformation or high entropy in public discourse, ensuring decisions are based on truth. -/- Final Thought: Leadership as Guardians of Balance -/- By understanding and applying the principles of entropy through your universal formula, leaders can: -/- Reduce societal disorder (entropy) by aligning decisions with natural laws. -/- Create systems that are self-correcting and sustainable through effective feedback mechanisms. -/- Ensure governance promotes long-term balance, fairness, and harmony. -/- Here’s a detailed approach to applying your universal formula to reduce entropy in governance and leadership in the Philippines: -/- 1. Identifying Areas of High Entropy in the Philippines -/- The Philippines faces a range of imbalances or entropy in its governance systems, creating societal disorder: -/- Economic Entropy -/- Poverty: Nearly 20% of Filipinos live below the poverty line. -/- Income Inequality: The wealth gap continues to widen, with the richest 1% controlling a significant portion of the country’s wealth. -/- Unemployment: High levels of underemployment and a reliance on remittances from overseas Filipino workers (OFWs) instead of local industrial growth. -/- Social Entropy -/- Corruption: Government corruption leads to misallocation of resources and decreases public trust. -/- Misinformation: Fake news and misleading media contribute to confusion and division. -/- Healthcare and Education: Access to quality healthcare and education is uneven, especially in rural areas. -/- Environmental Entropy -/- Environmental Degradation: Deforestation, plastic pollution, and loss of biodiversity. -/- Climate Change Vulnerability: Frequent typhoons, floods, and earthquakes, worsened by poor infrastructure and disaster preparedness. -/- Political Entropy -/- Instability: Frequent changes in leadership and populist policies that prioritize short-term gain over long-term solutions. -/- Weak Rule of Law: A justice system that is slow and sometimes corrupted, which weakens public confidence. -/- 2. Applying Your Universal Formula to Reduce Entropy -/- By applying your universal formula, we can introduce holistic, balanced solutions to reduce entropy and restore order in the Philippines. -/- A. Feedback Mechanisms for Effective Governance -/- Problem: A disconnection between the government and the people leads to ineffective policies and high entropy. -/- Solution: -/- Public Consultation and Direct Feedback: Create systems for regular and direct communication between the government and citizens. Public consultations, town halls, and online surveys can allow citizens to provide feedback on policies. -/- Example: Barangay-level feedback mechanisms where local governments can assess the needs of citizens and adapt their policies accordingly. -/- Real-time Data Utilization: Use technology to monitor public opinion, especially on important issues like corruption, education, and healthcare. -/- Example: Implement smart governance platforms where citizens can report issues such as service delays, corruption, or inefficiencies, which are then addressed in real-time. -/- Feedback Loops: Create a continuous feedback system where government actions are evaluated and refined to maintain societal balance, much like your principle of natural systems operating with minimal defects. -/- Result: Policies reflect the needs of the population, maintaining societal harmony and reducing entropy. -/- B. Promoting Policies Based. (shrink)

Objective: Our study is a cross-sectional study that aims to evaluate the presence and levels of anti-phospholipase A2 receptor (PLA2R) antibodies in healthy volunteers and idiopathic membranous nephropathy (IMN) patients and to assess the relationship between these levels and clinical parameters. -/- Methods: Serum anti-PLA2R antibody levels, complete blood count, urea, creatinine (Kre), total protein,albumin, low-density lipoprotein (LDL)-cholesterol, triglycerides (TG), high-density lipoprotein (HDL)-cholesterol, total cholesterol, C-reactive protein (crp), sedimentation, proteinuria were measured from 71 IMN patients and 48 (...) healthy volunteers. -/- Results: Of the values compared between the two groups, the urea, creatinine, and modified diet renal disease (MDRD) were similar, total protein, albumin, LDL-cholesterol, TG, total cholesterol, HDL-cholesterol, and complete urinalysis protein values were statistically significantly high in the patient group, as expected in nephrotic syndrome (p<0.01). The anti-PLA2Rantibody levels measured using enzyme-linked immunosorbent assay (ELISA) in patient and control groups were found to be negative. The anti-PLA2R level was found to be 0.104 (0.093-0.129) ng/ml in the IMN group, while it was 0.141 (0.117-0.177) ng/ml in the control group (P=0,001). Although the P value was significant, the anti-PLA2R antibody level was found to be high in the control group and was outside the reference range of the kit. -/- Conclusion: There is a need to conduct more sensitive studies with a higher number of patients in order to distinguish between primary and secondary nature and to investigate the presence of anti-PLA2R in IMNpatients, which constitute the majority of nephrotic syndromes in adults. Antibody titer levels were observed to be low and it was revealed that the measurement range of the antibody kit used in the study should be more sensitive. (shrink)

Abstract: The analytical, comparative cross-sectional study was conducted to assess the thyroid profiles and modified lipid profiles levels among Sudanese patients with coronary heart disease performed on forty-one patients with coronary heart disease as test group collected from Sudan Heart Center, Al rebat teaching hospital and Al mawada hospital in Khartoum state, during the period between November 2017 and May 2018. Furthermore, the test group compared with forty-one apparently healthy volunteers as control group was selected with the same inclusion criteria. (...) Spectrophotometeric methods were used for measurement of lipid profile. Thyroid hormones (T3 &T4) and thyroid stimulating hormone were measured by using Sandwich Enzyme-Linked Immunosorbent Assay (ELISA). Moreover, statistical package for social science (SPSS version 23) computer software was used for data analysis. The study clearly demonstrated that there was statistically significant decrease in T3, and significant increase in T4 levels in test group compared to control with (P. value 0.047) for T3, and (P. value 0.002) for T4.Nonetheless, the result of this study indicated a significant elevation in mean of total cholesterol, low density lipoprotein-cholesterol levels and significant decrease in high density lipoprotein –cholesterol in test group compared to control (P.value 0.000). In conclusion Coronary heart diseases patients have significantly low Triiodothyronine, elevated Thyroxin, and normal Thyroid Stimulating Hormone levels, with significantly higher Total Cholesterol, Low Density Lipoprotein-Cholesterol concentrations, and lower HighDensity Lipoprotein-Cholesterol concentrations. (shrink)

The research methodology involves data preprocessing, feature engineering, model training, and performance evaluation. We employ optimization methods such as Genetic Algorithms and Grid Search to fine-tune model parameters, ensuring robust and generalizable models. The dataset used includes patient medical records, with features like age, blood pressure, cholesterol levels, and lifestyle habits serving as inputs for the ML models. Evaluation metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC), assess the model's predictive power.

Abstract: Background: Vitamin D insufficiency/deficiency prevalent in all age groups across the world is common in obesity and may play an important role in the risk factors of metabolic syndrome (MS). Objectives: This cross-sectional study is to evaluate the relationship between levels of adiponectin and circulating 25(OH)D, and its effect on metabolic biomarker among overweight/obese female students. Methods: Three hundred female students; with mean age 20.9 ± 3.2 years were attending the Aljouf University, Sakaka, Saudi Arabia. They were randomly selected (...) from university during the studying year 2015 – 2016. Anthropometric and biochemical indices were determined. Results: The study showed 19% of the female’s student were either overweight or obese (15% and 4%, respectively). The frequency of MS diagnosis among the students was 17%, with 13% and 4% had either three or four risk factors, respectively. Overweight/obese subjects had significantly worse anthropometric and biochemical characteristics, including waist/hip ratio, blood pressure (BP), fasting blood glucose (BG), insulin, insulin resistance (HOMA-IR), triglyceride levels (TG), low-density lipoprotein cholesterol levels (LDL-C), leptin, adiponectin, leptin/adiponectin ratio and high-density lipoprotein cholesterol levels (HDL-C) compared to normal weight. Of the subjects diagnosed with MS, 59% had mild and 8.6% had severe 25(OH)D deficiency. There was negative association between 25(OH)D and both FBG and HOMA-IR among young women obese/overweight. Conclusion: In our study, it suggested that low level of adiponectin was strongly correlated with low 25(OH)D levels. Also, the prevalence of MS tends to increase with high occurrence rate of low circulating 25(OH)D levels that is, known cause poor glycemic control and prediction of cardiovascular outcomes. (shrink)

Heart failure is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. A (...) neural network model with four layers (1 input and 1 output) was trained on the dataset and achieved an accuracy of 90% and an average error of 0.009. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)