A disease prediction apparatus (100; 300; 400), comprising: a spectrometer (110; 310; 410) configured to obtain an optical spectrum of a skin of a subject; and a processor (120; 320; 420) configured to predict autofluorescence of the subject based on the optical spectrum by applying the optical spectrum to an autofluorescence prediction model, and predict a risk of developing a cardiovascular disease based on the predicted autofluorescence, wherein the autofluorescence prediction model is a prediction model, which is pre-generated by using at least one skin spectrum, obtained from at least one test subject by radiating a ultraviolet A, UV-A, light in a wavelength range of 320 nm to 380 nm to a test subject, and by using a measured value of autofluorescence emanating from a skin of the test subject after the UV-A light is radiated thereto, wherein the autofluorescence prediction model is generated by analyzing a correlation between the skin spectrum, obtained from the test subject, and autofluorescence by using a partial least square, PLS, regression algorithm, wherein the processor is further configured to select, based on user information, the autofluorescence prediction model from autofluorescence prediction models that are classified into one or more groups according to at least one of a user's age, gender, race, occupation, stature, body mass index, BMI, smoking status, hemoglobin A1c, HbA1c, concentration in the blood, and health information, wherein the user information comprises at least one of age, gender, race, occupation, stature, body mass index, BMI, smoking status, hemoglobin A1c, HbA1c, concentration of the subject and health information. a spectrometer (110; 310; 410) configured to obtain an optical spectrum of a skin of a subject; and a processor (120; 320; 420) configured to predict autofluorescence of the subject based on the optical spectrum by applying the optical spectrum to an autofluorescence prediction model, and predict a risk of developing a cardiovascular disease based on the predicted autofluorescence, wherein the autofluorescence prediction model is a prediction model, which is pre-generated by using at least one skin spectrum, obtained from at least one test subject by radiating a ultraviolet A, UV-A, light in a wavelength range of 320 nm to 380 nm to a test subject, and by using a measured value of autofluorescence emanating from a skin of the test subject after the UV-A light is radiated thereto, wherein the autofluorescence prediction model is generated by analyzing a correlation between the skin spectrum, obtained from the test subject, and autofluorescence by using a partial least square, PLS, regression algorithm, wherein the processor is further configured to select, based on user information, the autofluorescence prediction model from autofluorescence prediction models that are classified into one or more groups according to at least one of a user's age, gender, race, occupation, stature, body mass index, BMI, smoking status, hemoglobin A1c, HbA1c, concentration in the blood, and health information, wherein the user information comprises at least one of age, gender, race, occupation, stature, body mass index, BMI, smoking status, hemoglobin A1c, HbA1c, concentration of the subject and health information. <EOS>