КОРОНАРЛЫҚ ЖҮРЕК АУРУЫН БОЛЖАУДА МАШИНАЛЫҚ ОҚЫТУ ӘДІСТЕРІНІҢ САЛЫСТЫРМАЛЫ ТАЛДАУЫ: UCI HEART DISEASE ДЕРЕКТЕР ЖИЫНЫ НЕГІЗІНДЕ

Коронарлық жүрек ауруы әлемдік өлім мен мүгедектіктің негізгі себептерінің бірі болып қала береді. Уақтылы диагностика асқынуларды азайтып, денсаулық сақтау жүйесіне түсетін жүктемені жеңілдетеді. Дегенмен дәстүрлі әдістер қымбат, инвазивті және қолжетімділігі шектеулі. Соңғы зерттеулер машиналық оқытудың клиникалық тәжірибеде қолданылу мүмкіндігін растайды. Осыған байланысты тек клинико-демографиялық деректерді пайдаланып, визуализациясыз ауруды сенімді болжау мүмкін бе деген сұрақ туындайды.
Бұл жұмыстың мақсаты – мұндай модельдердің дәлдігі мен қолданбалық құндылығын бағалау. Біріктірілген протокол бойынша UCI Heart Disease деректер жиынында (n = 920) LightGBM алгоритмі оқытылып, мына нәтижелерге жетті: accuracy = 0.8696, precision = 0.8679, recall = 0.9020, F1-score = 0.8846. Нәтижелер алдыңғы визуализацияға сүйенген зерттеулерді толықтырды.
Зерттеу бірнеше алгоритмді бірдей алдын ала өңдеу мен валидация шарттарында салыстырды, ықтималдықтардың калибрленуін тексерді және SHAP әдісі арқылы интерпретация жасады. Талдау негізгі предикторлардың (мысалы, ST-сегмент депрессиясы) клиникалық біліммен сәйкес екенін көрсетті. Бұл модельді бастапқы скрининг пен қосымша диагностикаға жолдауда қолдануға болатынын дәлелдейді. Жалпы, ашық клиникалық деректерге негізделген калибрленген әрі түсіндірілетін алгоритмдер ресурсы шектеулі жағдайларда пациенттерді маршрутизациялауда құнды құрал бола алады.

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