1.Introduction

Obesity is a serious and chronic disease with genetic interactions. It is defined as an excessive amount of fat tissue in the body that is harmful to health. The main risk factors for obesity include social, psychological, and eating habits. Obesity is a significant health problem for all age groups in the world. This data-set includes lifestyle factors, boi-metric data, dietary habits and transportation choices of nearly 1610 individuals.Some of the factors used in this data are as follows: Overweight/Obese Families

• Consumption of Fast Food
• Frequency of Consuming Vegetables
• Number of Main Meals Daily
• Food Intake Between Meals
• Smoking
• Liquid Intake Daily
• Calculation Of Calorie Intake
• Physical Exercise
• Schedule Dedicated to Technology
• Type of Transportation Used
• Class

2.PLots of Age,Height and Family History of Obesity by Gender by Sex

The above two box plots gives us a comparison analysis of age and height between the males and females among the 1610 individuals.Both males and females have similar age distribution with a median age of mid 30’s. whereas the height distribution shows a notable difference , with males taller than females.

Family History of Obesity: This plot illustrates that for both sexes, individuals with no family history of overweight or obesity are more dominant than those with such a family history, with a larger number of females in each category than males.

3.Analyzing Gender Patterns in Fast Food and Vegetable Consumption Frequency

The visualization provides that a significant portion of both sexes do not consume fast food, with females in particular having a higher count in the “No” category.In the above visualization highlights that while both genders have similar patterns, females tend to report a slightly higher frequency of always consuming vegetables.

4.Meal and Snacking Habits by Gender

Both sexes generally have three main meals a day. Females show a marginally higher tendency towards frequent snacking. This combined pattern shows us the gender-based differences in eating habits, with a consistent eating meals across genders, but snacking behavior is slightly higher .

5.Gender-Based Analysis of Hydration and Calorie Tracking Habits

Both genders tend to take more than 2 liters of liquids daily, calorie intake tracking is less used, with most individuals from both sexes not engaging in this practice.The above plot shows us sufficient hydration and low engagement with calorie monitoring across genders.

6.Gender-Based Analysis of Patterns in Exercise and Technology Use

Both genders tend to exercise regularly and use technology moderately, with slight variations. Females have a slightly higher interest in both frequent physical activity and moderate use of technology.

7.Gender-Based Analysis of Smoking Prevalence and Preferred Transportation Modes

The above plot implies that smoking is more uniformly distributed among males, while females are mostly non-smokers.

Both genders mostly depend on automobiles and public transport,while males show preference for motorbikes and females are more favorable for biking and walking.

8.Strategies for Obesity Prevention and Management

-Increasing Physical Activity also integrating various types like aerobic and strength training.

-Improve Dietary Choices by limiting fast food and more intake of balanced meals rich in vegetables and proteins.

-Staying hydrated by drinking at-least 2 liters of water daily to support metabolism.

-Reducing screen time by taking regular breaks from screens and integrating a short physical activities.

-Educate on portion sizes and caloric content to help manage intake with strict calorie counting.

-BY quitting smoking to improve overall health and energy levels.

-Promote walking and add physical activity into daily routines by setting Health Goals

9.Conclusion

The visualizations provide a detailed overview of lifestyle factors supporting to obesity across genders. The main observations include differences in exercise regularity, dietary habits, hydration, smoking, technology use, and transportation preferences. Both males and females show similar patterns in basic habits like meal regularity and hydration, though females incline to involve slightly more in regular physical activity and consume vegetables more frequently. However, both genders show high screen time, limited calorie tracking, and a dependence on automobiles, which are underlying risk factors for inactive lifestyles.

We can conclude that while both genders have areas where they are taking positive steps, there are several lifestyle modifications that could help prevent and manage obesity more effectively. By focusing on increasing physical activity, improving dietary awareness, reducing inactive behaviors, and can support healthier lifestyles and reduce obesity risk across both genders.

References:

Koklu, N., & Sulak, S. A. (2024). Using artificial intelligence techniques for the analysis of obesity status according to the individuals’ social and physical activities. Sinop Üniversitesi Fen Bilimleri Dergisi, 9(1), 217–239. https://doi.org/10.33484/sinopfbd.1445215