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Lung cancer is a major cause of morbidity and mortality worldwide. Lung cancers may present with metastases in various locations of the body. The main metastasis areas of lung cancers are liver, adrenal glands, brain and skeletal system. This paper presents lung cancer detection using the symptoms occur in tongue and hand. Metastasis and Acrometastasis diseases were presented as the tumor in the tongue and hands of the human body. Acrometastasis to the hand is an unusual presentation which might mimic an infectious, inflammatory, or a metabolic pathology. Tongue metastasis is extremely rare as an initial manifestation of the disease. This work proposes Hybrid Bayesian nearest neighbor model classifier(HBNK) to predict the lung cancer using tongue and hand images. Tongue segmentation is performed using Morphological based random walker segmentation and the hand segmentation is performed using sobel edge detection method. To train the classifier ORB features, color diversity features and the chromatic features are extracted. The experimental results shows that the performance of the proposed method. Putting HBNK on IoT Thingspeak platform will help doctors to diagnose tongue and hand images that are received from computerized system online. The obtained results of proposed method are compared with KNN algorithm.