Validasi Efektivitas Logistic Regression untuk Diagnosa Penyakit Jantung melalui Pendekatan Machine Learning
DOI:
https://doi.org/10.22441/fifo.2024.v16i2.006Keywords:
Logistic Regression, Machine Learning, Neural Network, Prediksi Penyakit Jantung, UCI Heart DiseaseAbstract
Penelitian yang bertujuan untuk mengevaluasi efektivitas Logistic Regression dalam diagnosa penyakit jantung telah dilakukan dengan menggunakan dataset UCI Heart Disease. Dataset ini terdiri dari 303 data pasien dengan 14 fitur, termasuk usia, jenis kelamin, tekanan darah, dan kolesterol, yang dibagi menjadi 60% data pelatihan, 20% data validasi dan 20% data pengujian. Penelitian dimulai dengan perumusan masalah dan studi literatur, diikuti oleh proses preprocessing data yang mencakup imputasi nilai hilang, normalisasi fitur, dan pembagian dataset. Beberapa model machine learning, yaitu k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, dan Neural Network, diterapkan dan dibandingkan. Model dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1 score. Hasil penelitian menunjukkan bahwa Logistic Regression memperoleh akurasi tertinggi sebesar 0.89, dengan keunggulan dalam presisi dan recall. Naïve Bayes dan kNN menunjukkan akurasi masing-masing 0.87 dan 0.85, sedangkan Neural Network memperoleh akurasi terendah 0.77. Tuning hyperparameter pada Neural Network tidak menunjukkan pola yang jelas dalam meningkatkan akurasi. Pada penelitian ini Logistic Regression adalah model yang paling efektif untuk prediksi penyakit jantung dibandingkan dengan model lainnya, terutama dalam konteks aplikasi medis yang membutuhkan interpretabilitas dan efisiensi.
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