Postingan

Predictive Modeling of Body Mass Index and Obese Status using Anthropometric, Dietary, and Lifestyle Factors in U.S. Adults

Predictive Modeling of Body Mass Index and Obese Status using Anthropometric, Dietary, and Lifestyle Factors in U.S. Adults The escalating global prevalence of obesity remains a critical public health challenge, driving increased morbidity and mortality associated with chronic diseases such as type 2 diabetes, cardiovascular disease, and certain cancers. Understanding the complex interplay of factors contributing to body mass index (BMI) and the progression to obese status is paramount for developing effective, targeted public health interventions. Traditional linear models often fail to capture the nuances of these relationships, necessitating the use of advanced predictive and clustering methods to identify high-risk subgroups and key non-linear determinants. Research Question Statement This study addresses the following research question: What is the differential association of key quantitative and categorical health and lifestyle variables—specifically age, gender, waist circumfere...

Analisis K-Means Cluster

🧩 Analisis K-Means Cluster 1. Penentuan Jumlah Klaster (K) Kami menggunakan Elbow Method (berdasarkan Within-Cluster Sum of Squares atau WCSS) untuk memilih jumlah klaster optimal ( $K$ ). Python # Output WCSS untuk Elbow Method 1 40145.000000 2 34404.436455 3 32218.313741 4 30097.198378 <-- Pilihan K= 4 5 28770.359263 6 27453.602716 Berdasarkan output WCSS, penurunan terbesar terjadi dari $K=1$ ke $K=2$ . Penurunan mulai melandai setelah $K=4$ . Kami memilih $\mathbf{K=4}$ sebagai jumlah klaster yang optimal, menawarkan keseimbangan yang baik antara mengurangi variasi dalam klaster dan menjaga interpretasi. 2. Sintaks K-Means Cluster Analysis (K=4) Data telah diskala ( standardized ) terlebih dahulu, yang merupakan langkah wajib dalam $K\text{-Means}$ untuk memastikan semua variabel memiliki bobot yang sama dalam perhitungan jarak. Python # Sintaks K-Means Cluster Analysis (K=4) K = 4 kmeans = KMeans(n_clusters=K, init= 'k-means++'...